Arrhythmia Patterns in Patients on Ibrutinib

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.

Prediction model for thyrotoxic atrial fibrillation: a retrospective study

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.

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

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.

Prediction model for thyrotoxic atrial fibrillation: a retrospective study.

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.

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

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.

Evaluation of noninvasive electrophysiological imaging accuracy for focal atrial arrhythmias

Background High accuracy of noninvasive electrocardiographic imaging (ECGI) has recently been shown for topical diagnostics of ventricular arrhythmias. However, the precision of diagnostics of atrial focal arrhythmias requires clarification. To estimate the accuracy of ECGI for premature atrial contraction (PAC) we performed atrial pacing in patients with CRT system and compared early activation zone (EAZ) with pacemaker's tip location. Purpose To determine the accuracy of ECGI for focal atrial arrhythmias using atrial pacing. Methods Twenty-six patients (m/f – 18/9), age (min–max) 52 (26–78) with CRT system and pacemaker's tip location in the right atrium (RA) appendage underwent ECGI (“Amycard 01C”) in combination with CT or MR imaging. Thirty-four atrial pacing (mono- and bipolar) was performed in all patients using standard amplitude 1.5–3.8 mV. Epi-/endocardial polygonal heart models were created and isopotential maps were calculated. The distance between EAZ and the pacemaker's tip were measured for ECG recordings without using the isoline filter on endocardial surface (Fig. 1) as well as for epicardial surface. The time between epicardial and endocardial EAZ breakthrough was calculated also. Results On endocardial surface the EAZ was located in RA appendage, the base of superior cava vena or superior lateral RA wall. The distance (mm) (Me (min; max)) between EAZ and the pacemacer's tip was 28 (6; 68). For epicardial surface in most cases the EAZ was also located in RA appendage, the base of superior cava vena or superior lateral RA wall. In two cases the EAZ was located in inferior septal RA wall, in one case - in superior septal RA wall and in five cases the EAZ was undetectable. The distance between EAZ and the pacemacer's tip was 22 (6; 48). The time (ms) (Mean; Me (min; max)) between EAZ of the endocardial and epicardial surfaces was 16; 7 (0; 68). Conclusion ECGI allows to assess the location of focal atrial arrhythmias on endocardial surface and sometimes on epicardial surface also within the three segments. The results of this study revealed that accuracy of ECGI for atrial arrhythmias is worse than for ventricular arrhythmias. However, it is better on epicardial surface of atrium when EAZ can be determined. Funding Acknowledgement Type of funding source: None