scholarly journals Arrhythmia Patterns in Patients on Ibrutinib

2022 ◽  
Vol 8 ◽  
Author(s):  
Muhammad Fazal ◽  
Ridhima Kapoor ◽  
Paul Cheng ◽  
Albert J. Rogers ◽  
Sanjiv M. Narayan ◽  
...  
Keyword(s):  
Kinase Inhibitor ◽  
Premature Ventricular Contraction ◽  
Treatment Interruption ◽  
Hematologic Malignancies ◽  
Retrospective Data Analysis ◽  
Atrial Contraction ◽  
Premature Atrial Contraction ◽  
High Prevalence ◽  
High Incidence

Introduction: Ibrutinib, a Bruton's tyrosine kinase inhibitor (TKI) used primarily in the treatment of hematologic malignancies, has been associated with increased incidence of atrial fibrillation (AF), with limited data on its association with other tachyarrhythmias. There are limited reports that comprehensively analyze atrial and ventricular arrhythmia (VA) burden in patients on ibrutinib. We hypothesized that long-term event monitors could reveal a high burden of atrial and VAs in patients on ibrutinib.Methods: A retrospective data analysis at a single center using electronic medical records database search tools and individual chart review was conducted to identify consecutive patients who had event monitors while on ibrutinib therapy.Results: Seventy-two patients were included in the analysis with a mean age of 76.9 ± 9.9 years and 13 patients (18%) had a diagnosis of AF prior to the ibrutinib therapy. During ibrutinib therapy, most common arrhythmias documented were non-AF supraventricular tachycardia (n = 32, 44.4%), AF (n = 32, 44%), and non-sustained ventricular tachycardia (n = 31, 43%). Thirteen (18%) patients had >1% premature atrial contraction burden; 16 (22.2%) patients had >1% premature ventricular contraction burden. In 25% of the patients, ibrutinib was held because of arrhythmias. Overall 8.3% of patients were started on antiarrhythmic drugs during ibrutinib therapy to manage these arrhythmias.Conclusions: In this large dataset of ambulatory cardiac monitors on patients treated with ibrutinib, we report a high prevalence of atrial and VAs, with a high incidence of treatment interruption secondary to arrhythmias and related symptoms. Further research is warranted to optimize strategies to diagnose, monitor, and manage ibrutinib-related arrhythmias.

scholarly journals Autoencoder-Based Extrasystole Detection and Modification of RRI Data for Precise Heart Rate Variability Analysis

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3235
Author(s):  
Koichi Fujiwara ◽  
Shota Miyatani ◽  
Asuka Goda ◽  
Miho Miyajima ◽  
Tetsuo Sasano ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Mean Squared Error ◽  
False Positive Rate ◽  
Premature Ventricular Contraction ◽  
Heart Rate Variability Analysis ◽  
Unified Framework ◽  
Atrial Contraction ◽  
Premature Atrial Contraction ◽  
Positive Rate

Heart rate variability, which is the fluctuation of the R-R interval (RRI) in electrocardiograms (ECG), has been widely adopted for autonomous evaluation. Since the HRV features that are extracted from RRI data easily fluctuate when arrhythmia occurs, RRI data with arrhythmia need to be modified appropriately before HRV analysis. In this study, we consider two types of extrasystoles—premature ventricular contraction (PVC) and premature atrial contraction (PAC)—which are types of extrasystoles that occur every day, even in healthy persons who have no cardiovascular diseases. A unified framework for ectopic RRI detection and a modification algorithm that utilizes an autoencoder (AE) type of neural network is proposed. The proposed framework consists of extrasystole occurrence detection from the RRI data and modification, whose targets are PVC and PAC. The RRI data are monitored by means of the AE in real time in the detection phase, and a denoising autoencoder (DAE) modifies the ectopic RRI caused by the detected extrasystole. These are referred to as AE-based extrasystole detection (AED) and DAE-based extrasystole modification (DAEM), respectively. The proposed framework was applied to real RRI data with PVC and PAC. The result showed that AED achieved a sensitivity of 93% and a false positive rate of 0.08 times per hour. The root mean squared error of the modified RRI decreased to 31% in PVC and 73% in PAC from the original RRI data by DAEM. In addition, the proposed framework was validated through application to a clinical epileptic seizure problem, which showed that it correctly suppressed the false positives caused by PVC. Thus, the proposed framework can contribute to realizing accurate HRV-based health monitoring and medical sensing systems.

scholarly journals Prediction model for thyrotoxic atrial fibrillation: a retrospective study.

2021 ◽  
Author(s):  
Daria Aleksandrovna Ponomartseva ◽  
Ilia Vladislavovich Derevitskii ◽  
Sergey Valerevich Kovalchuk ◽  
Alina Yurevna Babenko
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Heart Rate ◽  
Retrospective Study ◽  
Prediction Model ◽  
Performance Metrics ◽  
Premature Ventricular Contraction ◽  
Ventricular Contraction ◽  
Atrial Contraction ◽  
Premature Atrial Contraction

Abstract Background: Thyrotoxic atrial fibrillation (TAF) is a recognized significant complication of hyperthyroidism. Early identification of the individuals predisposed to TAF would improve thyrotoxic patients’ management. However, to our knowledge, an instrument that establishes an individual risk of the condition is unavailable. Therefore, the aim of this study is to build a TAF prediction model and rank TAF predictors in order of importance. Methods: In this retrospective study, we have investigated 36 demographic and clinical features for 420 patients with overt hyperthyroidism, 30% of which had TAF. At first, the association of these features with TAF was evaluated by classical statistical methods. Then, we developed several TAF prediction models with eight different machine learning classifiers and compared them by performance metrics. The models included ten features that were selected based on their clinical effectuality and importance for model output. Finally, we ranked TAF predictors, elicited from the optimal final model, by the machine learning tehniques. Results: The best performance metrics prediction model was built with the extreme gradient boosting classifier. It had the reasonable accuracy of 84% and AUROC of 0.89 on the test set. The model confirmed such well-known TAF risk factors as age, sex, hyperthyroidism duration, heart rate and some concomitant cardiovascular diseases (arterial hypertension and conjestive heart rate). We also identified premature atrial contraction and premature ventricular contraction as new TAF predictors. The top five TAF predictors, elicited from the model, included (in order of importance) PAC, PVC, hyperthyroidism duration, heart rate during hyperthyroidism and age. Conclusions: We developed a machine learning model for TAF prediction. It seems to be the first available analytical tool for TAF risk assessment. In addition, we defined five most important TAF predictors, including premature atrial contraction and premature ventricular contraction as the new ones. These results have contributed to TAF prediction investigation and may serve as a basis for further research focused on TAF prediction improvement and facilitation of thyrotoxic patients’ management.

scholarly journals Prediction model for thyrotoxic atrial fibrillation: a retrospective study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Daria Aleksandrovna Ponomartseva ◽  
Ilia Vladislavovich Derevitskii ◽  
Sergey Valerevich Kovalchuk ◽  
Alina Yurevna Babenko
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Heart Rate ◽  
Retrospective Study ◽  
Prediction Model ◽  
Performance Metrics ◽  
Premature Ventricular Contraction ◽  
Ventricular Contraction ◽  
Atrial Contraction ◽  
Premature Atrial Contraction

Abstract Background Thyrotoxic atrial fibrillation (TAF) is a recognized significant complication of hyperthyroidism. Early identification of the individuals predisposed to TAF would improve thyrotoxic patients’ management. However, to our knowledge, an instrument that establishes an individual risk of the condition is unavailable. Therefore, the aim of this study is to build a TAF prediction model and rank TAF predictors in order of importance using machine learning techniques. Methods In this retrospective study, we have investigated 36 demographic and clinical features for 420 patients with overt hyperthyroidism, 30% of which had TAF. At first, the association of these features with TAF was evaluated by classical statistical methods. Then, we developed several TAF prediction models with eight different machine learning classifiers and compared them by performance metrics. The models included ten features that were selected based on their clinical effectuality and importance for model output. Finally, we ranked TAF predictors, elicited from the optimal final model, by the machine learning tehniques. Results The best performance metrics prediction model was built with the extreme gradient boosting classifier. It had the reasonable accuracy of 84% and AUROC of 0.89 on the test set. The model confirmed such well-known TAF risk factors as age, sex, hyperthyroidism duration, heart rate and some concomitant cardiovascular diseases (arterial hypertension and conjestive heart rate). We also identified premature atrial contraction and premature ventricular contraction as new TAF predictors. The top five TAF predictors, elicited from the model, included (in order of importance) PAC, PVC, hyperthyroidism duration, heart rate during hyperthyroidism and age. Conclusions We developed a machine learning model for TAF prediction. It seems to be the first available analytical tool for TAF risk assessment. In addition, we defined five most important TAF predictors, including premature atrial contraction and premature ventricular contraction as the new ones. These results have contributed to TAF prediction investigation and may serve as a basis for further research focused on TAF prediction improvement and facilitation of thyrotoxic patients’ management.

scholarly journals The role of Helicobacter pylori in metabolic and cardiovascular diseases

2021 ◽  
pp. 86-95
Author(s):  
T. E. Kugler ◽  
G. G. Taradin ◽  
R. Pellicano
Keyword(s):  
Helicobacter Pylori ◽  
Cardiovascular Diseases ◽  
Diagnostic Methods ◽  
Alcoholic Fatty Liver ◽  
High Prevalence ◽  
High Incidence ◽  
H Pylori ◽  
Alcoholic Fatty Liver Disease

Helicobacter pylori (H. pylori) infection is one of the most common in the world. More than 50% of the world’s population is infected and infection rates are especially high in countries with poor socio-economic conditions. H. pylori causes gastroduodenal and extra-gastroduodenal diseases including such metabolic disorders as obesity, diabetes mellitus and non-alcoholic fatty liver disease. Recent epidemiological and clinical studies showed that the long-term persistence of H. pylori infection is associated with the development and progression of atherosclerosis and consequently cardiovascular diseases. However, the correlation between these conditions is ambiguous and there is not enough evidence to confirm it. The lack of consensus might be related to differences in diagnostic methods used for H. pylori and the variety of its genotypes. Considering high prevalence of H. pylori infection as well as high incidence of metabolic and cardiovascular diseases, a verified correlation between these can be of great epidemiological, prophylactic and clinical significance.

A Clinical Study on Atrial Fibrillation, Premature Ventricular Contraction, and Premature Atrial Contraction Screening Based on an ECG Deep Learning Model (Preprint)

2021 ◽  
Author(s):  
Jianyuan Hong ◽  
Hua-Jung Li ◽  
Chung-Chi Yang ◽  
Chi-Lu Han ◽  
Jui-Chien Hsieh
Keyword(s):  
Atrial Fibrillation ◽  
Deep Learning ◽  
Premature Ventricular Contraction ◽  
Learning Model ◽  
Training Dataset ◽  
Ventricular Contraction ◽  
Atrial Contraction ◽  
Premature Atrial Contraction ◽  
Sensitivity Specificity ◽  
Deep Learning Model

BACKGROUND As the results of this study indicate, electrocardiography (ECG) devices generating interpretations of atrial fibrillation (AF), premature ventricular contraction (PVC), and premature atrial contraction (PAC) have high ratios of false-positive errors. OBJECTIVE The aim of this study was to develop an electrocardiogram (ECG) interpreter to improve the performance of AF, PVC, and PAC screening based on an ECG. METHODS In this study, we first adopted a deep learning model to delineate ECG features such as P, QRS, and T waves based on 1160 8–10-s lead I or lead II ECG signals whose ECG device interpretation is AF as a training dataset. Second, a sliding window with 3-RR intervals in length is applied to the raw ECG to examine the delineated features in the window, and the ECG interpretation is then determined based on experiences of cardiologists. RESULTS The results indicate the following: (1) This delineator achieves a good performance on P-, QRS-, and T- wave delineation with a sensitivity/specificity of 0.94/0.98, 1.00/0.99, and 0.97/0.98, respectively, in 48 10-s test ECGs mixed with AF and non-AF ECGs. (2) As compared to ECG-device generated interpretations, the precision of the detection of AF, PVC, and PAC in this study was increased from 0.77 to 0.86, 0.76 to 0.84, and 0.82 to 0.87 in 188 10-s test ECGs. Finally, (3) the F1 scores on the detection of AF, PVC, and PAC were 0.92, 0.91, and 0.83, respectively. CONCLUSIONS In conclusion, this study improved the accuracy of ECG device interpretations, and we believe that the results can bridge the gap between research and clinical practice.

Apophysites – Revue de la littérature et ­nouvelles directions

2014 ◽  
Vol 62 (3) ◽  
Keyword(s):  
Scientific Evidence ◽  
Ossification Center ◽  
Health Staff ◽  
Optimal Management ◽  
Long Term Effect ◽  
High Incidence ◽  
Youth Athlete ◽  
Pathology Education ◽  
Main Factor

Apophysitis are part of the growth-related diseases within youth athlete population. Despite their high incidence within this growing cohort, many doubts remain. The physiopathology is still debated. Initially, the fragmentation of the ossification center was seen as the main factor of the disease. For few years, this theory has been questioned due to consistent signs of tendon suffering. Apophysitis may have some negative long-term effect on a sporting career. There is currently poor scientific evidence on the optimal management and no treatment has been widely accepted. Prevention remains the most powerful intervention in this particular pathology. Education of the athlete’s sporting entourage (family, coaches and health staff) and the athlete himself is necessary to act quickly and adapt the training load to decrease mechanical stress on the suffering apophysis.

scholarly journals Long‐term safety and efficacy of delgocitinib ointment, a topical Janus kinase inhibitor, in adult patients with atopic dermatitis

2019 ◽  
Vol 47 (2) ◽  
pp. 114-120 ◽  
Author(s):  
Hidemi Nakagawa ◽  
Osamu Nemoto ◽  
Atsuyuki Igarashi ◽  
Hidehisa Saeki ◽  
Ryusei Murata ◽  
...  
Keyword(s):  
Atopic Dermatitis ◽  
Kinase Inhibitor ◽  
Janus Kinase ◽  
Adult Patients ◽  
Janus Kinase Inhibitor ◽  
Safety And Efficacy
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