Diagnosis Of Arrhythmias Research Articles

The patient experience of in-hospital telemetry monitoring: a qualitative analysis.

In-hospital telemetry monitoring has been an integrated part of arrhythmia monitoring for decades. A substantial proportion of patients require arrhythmia monitoring during stays in non-intensive care units. However, studies exploring patients' experiences of telemetry monitoring are scarce. Therefore, the aim was to explore and describe patients' experiences of in-hospital telemetry monitoring in a non-intensive care setting. Twenty face-to-face, semi-structured interviews were conducted. Interviews were conducted before discharge at two university hospitals in Norway. The patients were purposively sampled, resulting in a well-balanced population comprising 11 men and nine women, mean age 62 years (range 25-83). Average monitoring time was 9 days (range 3-14). Data were audiotaped, transcribed verbatim, and coded using NVivo software. Qualitative content analysis using an inductive approach was performed. Patients expressed a need for individualized information during telemetry monitoring. Their feelings of safety were related to responses from nurses from the central monitoring station when alarms from the telemetry were triggered. Despite perceived physical restrictions and psychological limitations associated with telemetry monitoring, they found monitoring to be beneficial because it facilitated the diagnosis of arrhythmia. Moreover, they expressed a need for improvements in wearable monitoring equipment. Patients expressed ambivalent feelings about discontinuing the telemetry and their readiness for discharge. Patients need individualized information about the results of their telemetry monitoring in order to better understand the arrhythmia management and to increase their experience of safety after discharge. The limitations patients experienced should be taken into consideration in further upgrades of telemetry monitoring equipment.

A homologous and heterogeneous multi-view inter-patient adaptive network for arrhythmia detection

Background and ObjectiveElectrocardiogram (ECG) is a widely used diagnostic tool for arrhythmia assessment in clinical practice. However, current arrhythmia detection algorithms rely heavily on signal-based data, while cardiologists often use image-based data. This discrepancy, combined with individual differences in physiological signals, poses challenges for accurate arrhythmia detection. To address these challenges and improve arrhythmia detection performance, we propose a homologous and heterogeneous multi-view inter-patient adaptive network. MethodsWe designed a multi-view representation learning module to capture dynamic and morphological characteristics from ECG signals and electrocardiographic images. Expert knowledge was also elicited to gain internally-invariant characteristics of each category. Finally, we designed a new loss function that aligns the embedding of the source and target domains in the feature space to minimize the negative effects of individual differences. ResultsExperiments on the MIT-BIH arrhythmia database demonstrate that our proposed method outperforms state-of-the-art baselines in terms of accuracy, positive predictive value, sensitivity and F1-score. These results indicate the effectiveness of our method in accurately detecting arrhythmias. ConclusionsOur homologous and heterogeneous multi-view inter-patient adaptive network successfully addresses the challenges of utilizing both ECG signal and electrocardiographic image data for arrhythmia detection and overcoming individual differences in physiological signals. Our proposed method has the potential to improve early diagnosis and treatment outcomes of arrhythmias in clinical practice.

An old diagnostic tool for new indications: inpatient Holter ECG for conditions other than syncope or stroke

Holter electrocardiography (ECG) assists in the diagnosis of arrhythmias. Its use in the inpatient setting has been described solely for the evaluation of stroke and syncope. Our aim was to assess its diagnostic value for other conditions in the internal medicine department. We included all hospitalized patients between 2018 and 2021 in a tertiary referral center. The primary outcome was a diagnostic Holter recording a new arrhythmia that led to a change in treatment. Overall, 289 patients completed a 24-h inpatient Holter ECG for conditions other than syncope or stroke, with 39 (13%) diagnostic findings. The highest diagnostic value was found in patients admitted for pre-syncope (19%), palpitations (18%), and unexplained heart failure exacerbation/dyspnea (17%). A low diagnostic yield was found for the evaluation of chest pain (5%). Heart failure with preserved ejection fraction (adjusted OR 2.3, 95% CI 1.1–5.4, p = 0.04), and baseline ECG with either a bundle branch block (AOR 4.2, 95% CI 1.9–9.2, p < 0.01) or atrioventricular block (first or second degree, AOR 5, 95% CI 2.04–12.3, p < 0.01) were among the independent predictors for a diagnostic test. Inpatient Holter ECG monitoring may have value as a diagnostic tool for selected patients with conditions other than syncope or stroke.

A cardiologist-like computer-aided interpretation framework to improve arrhythmia diagnosis from imbalanced training datasets

Arrhythmias can pose a significant threat to cardiac health, potentially leading to serious consequences such as stroke, heart failure, cardiac arrest, shock, and sudden death. In computer-aided electrocardiogram interpretation systems, the inclusion of certain classes of arrhythmias, which we term "aggressive" or "bullying," can lead to the underdiagnosis of other "vulnerable" classes. To address this issue, a method for arrhythmia diagnosis is proposed in this study. This method combines morphological-characteristic-based waveform clustering with Bayesian theory, drawing inspiration from the diagnostic reasoning of experienced cardiologists. The proposed method achieved optimal performance in macro-recall and macro-precision through hyperparameter optimization, including spliced heartbeats and clusters. In addition, with increasing bullying by aggressive arrhythmias, our model obtained the highest average recall and the lowest average drop in recall on the nine vulnerable arrhythmias. Furthermore, the maximum cluster characteristics were found to be consistent with established arrhythmia diagnostic criteria, lending interpretability to the proposed method.

Artificial intelligence cloud platform improves arrhythmia detection from insertable cardiac monitors to 25 cardiac rhythm patterns through multi-label classification

BackgroundElectrocardiogram (ECG) is the gold standard for the diagnosis of cardiac arrhythmias and other heart diseases. Insertable cardiac monitors (ICMs) have been developed to continuously monitor cardiac activity over long periods of time and to detect 4 cardiac patterns (atrial tachyarrhythmias, ventricular tachycardia, bradycardia, and pause). However, interpretation of ECG or ICM subcutaneous ECG (sECG) is time-consuming for clinicians. Artificial intelligence (AI) classifies ECG and sECG with high accuracy in short times. ObjectiveTo demonstrate whether an AI algorithm can expand ICM arrhythmia recognition from 4 to many cardiac patterns. MethodsWe performed an exploratory retrospective study with sECG raw data coming from 20 patients wearing a Confirm Rx™ (Abbott, Sylmar, USA) ICM. The sECG data were recorded in standard conditions and then analyzed by AI (Willem™, IDOVEN, Madrid, Spain) and cardiologists, in parallel. ResultsIn nineteen patients, ICMs recorded 2261 sECGs in an average follow-up of 23 months. Within these 2261 sECG episodes, AI identified 7882 events and classified them according to 25 different cardiac rhythm patterns with a pondered global accuracy of 88%. Global positive predictive value, sensitivity, and F1-score were 86.77%, 83.89%, and 85.52% respectively. AI was especially sensitive for bradycardias, pauses, rS complexes, premature atrial contractions, and inverted T waves, reducing the median time spent to classify each sECG compared to cardiologists. ConclusionAI can process sECG raw data coming from ICMs without previous training, extending the performance of these devices and saving cardiologists' time in reviewing cardiac rhythm patterns detection.

Disparities in resource utilisation by families of children with cardiac conditions.

There are limited data documenting sources of medical information that families use to learn about paediatric cardiac conditions. Our study aims to characterise these resources and to identify any disparities in resource utilisation. We hypothesise there are significant variations in the resources utilised by families from different educational and socio-economic backgrounds. A survey evaluating what resources families use (websites, healthcare professionals, social media, etc.) to better understand paediatric cardiac conditions was administered to caretakers and paediatric patients at Morgan Stanley Children's Hospital. Patients with a prior diagnosis of CHD, cardiac arrhythmia, and/or heart failure were included. Caretakers' levels of education (fewer than 16 years vs. 16 years or more) and patients' medical insurance types (public vs. private) were compared with regard to the utilisation of resources. Surveys completed by 137 (91%) caretakers and 27 (90%) patients were analysed. Websites were utilised by 72% of caretakers and 56% of patients. Both private insurance and higher education were associated with greater reported utilisation of websites, healthcare professionals, and personal networks (by insurance p = 0.009, p = 0.001, p = 0.006; by education p = 0.022, p < 0.001, p = 0.018). They were also more likely to report use of electronic devices (such as a computer) compared to those with public medical insurance and fewer than 16 years of education (p < 0.001, p < 0.001, respectively). Both levels of education and insurance status are associated with the utilisation of informative resources and digital devices by families seeking to learn more about cardiac conditions in children.

An end-end arrhythmia diagnosis model based on deep learning neural network with multi-scale feature extraction.

This study presents an innovative end-to-end deep learning arrhythmia diagnosis model that aims to address the problems in arrhythmia diagnosis. The model performs pre-processing of the heartbeat signal by automatically and efficiently extracting time-domain, time-frequency-domain and multi-scale features at different scales. These features are imported into an adaptive online convolutional network-based classification inference module for arrhythmia diagnosis. Experimental results show that the AOCT-based deep learning neural network diagnostic module has excellent parallel computing and classification inference capabilities, and the overall performance of the model improves with increasing scales. In particular, when multi-scale features are used as inputs, the model is able to learn both time-frequency domain information and other rich information, thus significantly improving the performance of the end-to-end diagnostic model. The final results show that the AOCT-based deep learning neural network model has an average accuracy of 99.72%, a recall of 99.62%, and an F1 score of 99.3% in diagnosing four common heart diseases.

On Merging Feature Engineering and Deep Learning for Diagnosis, Risk Prediction and Age Estimation Based on the 12-Lead ECG.

Over the past few years, deep learning (DL) has been used extensively in research for 12-lead electrocardiogram (ECG) analysis. However, it is unclear whether the explicit or implicit claims made on DL superiority to the more classical feature engineering (FE) approaches, based on domain knowledge, hold. In addition, it remains unclear whether combining DL with FE may improve performance over a single modality. To address these research gaps and in-line with recent major experiments, we revisited three tasks: cardiac arrhythmia diagnosis (multiclass-multilabel classification), atrial fibrillation risk prediction (binary classification), and age estimation (regression). We used an overall dataset of 2.3M 12-lead ECG recordings to train the following models for each task: i) a random forest taking FE as input; ii) an end-to-end DL model; and iii) a merged model of FE+DL. FE yielded comparable results to DL while necessitating significantly less data for the two classification tasks. DL outperformed FE for the regression task. For all tasks, merging FE with DL did not improve performance over DL alone. These findings were confirmed on the additional PTB-XL dataset. We found that for traditional 12-lead ECG based diagnosis tasks, DL did not yield a meaningful improvement over FE, while it improved significantly the nontraditional regression task. We also found that combining FE with DL did not improve over DL alone, which suggests that the FE were redundant with the features learned by DL. Our findings provides important recommendations on 12-lead ECG based machine learning strategy and data regime to choose for a given task. When looking at maximizing performance as the end goal, if the task is nontraditional and a large dataset is available then DL is preferable. If the task is a classical one and/or a small dataset is available then a FE approach may be the better choice.

Accuracy of Single-lead ECG for the Diagnosis of Cardiac Arrhythmias Compared to the Gold Standard of Cardiac Electrophysiology Study

Accuracy of Single-lead ECG for the Diagnosis of Cardiac Arrhythmias Compared to the Gold Standard of Cardiac Electrophysiology Study

Chinese emergency expert consensus on bedside temporary cardiac pacing (2023)

Temporary cardiac pacing is an essential technique in the diagnosis and treatment of arrhythmias. Due to its urgency, complexity, and uncertainty, it is necessary to develop an evidence-based emergency operation norms. Currently, there is no specific consensus guidelines at home or abroad. The Emergency Branch of Chinese Medical Association organized relevant experts to draft the Chinese emergency expert consensus on bedside temporary cardiac pacing (2023) to guide the operation and application of bedside cardiac pacing. The formulation of the consensus adopts the consensus meeting method and the evidentiary basis and recommendation grading of the Oxford Center for Evidence-based Medicine in the United States. A total of 13 recommendations were extracted from the discussion on the methods of bedside temporary cardiac pacing, the puncture site of transvenous temporary cardiac pacing, the selection of leads, the placement and placement of leads, pacemaker parameter settings, indications, complications and postoperative management. The recommended consensus includes the choice between transcutaneous and transvenous pacing, preferred venous access for temporary transvenous pacing, the target and best guidance method for implantation of bedside pacing electrodes, recommended default pacemaker settings, recommended indications for sinoatrial node dysfunction, atrioventricular block, acute myocardial infarction, cardiac arrest, ventricular and supraventricular arrhythmias. They also recommended ultrasound guidance and a shortened temporary pacing support time to reduce complications of temporary transvenous cardiac pacing, recommended bedrest, and anticoagulation after temporary transvenous pacing. Bedside temporary cardiac pacing is generally safe and effective. Accurate assessment, correct selection of the pacing mode, and timely performance of bedside temporary cardiac pacing can further improve the survival rate and prognosis of related emergency patients.

Domain and Patient Adversarial Multi-Task Learning for Arrhythmia Classification.

Manual screening of electrocardiograms (ECGs) for heart arrhythmias by clinicians is time-consuming and labor-intensive. A machine learning model for the automated diagnosis of heart arrhythmia from ECG signals can facilitate improved diagnosis, greater accessibility and earlier intervention for patients. The potential of such models is limited however by the small size of clinical datasets available for training. Methods that can be trained with multiple datasets to classify heart arrhythmia are needed to overcome this problem.In this paper, we propose using adversarial multi-task learning (AMTL) to extract domain and patient invariant features from two electrocardiogram databases. We further investigated the influence of beat segmentation location and beat normalization on domain invariance. Our proposed methods were tested on the MIT-BIH Arrhythmia and the St Petersburg INCART 12-lead Arrhythmia Databases. The domain adversarial models achieved a higher accuracy and average F1 score than their counterparts without domain adversarial learning. In particular, the patient and domain adversarial model improved the F1 scores on the two tested databases from 70% and 74% to 77% each.Clinical Relevance-This establishes that adversarial multitask learning with multiple datasets and multiple adversarial tasks can improve the F1 score of arrhythmia classification.

Obstructive Sleep Apnea Patients With Atrial Arrhythmias Suffer From Prolonged Recovery From Desaturations

We aimed to investigate how acute and long-term effects of atrial arrhythmias affect the desaturation severity and characteristics determined from the oxygen saturation signal in OSA patients. 520 suspected OSA patients were included in retrospective analyses. Eight desaturation area and slope parameters were calculated from blood oxygen saturation signals recorded during polysomnographic recordings. Patients were grouped based on whether they had previously diagnosed atrial arrhythmia (i.e., atrial fibrillation (AFib) or atrial flutter) or not. Furthermore, patients with a previous atrial arrhythmia diagnosis were sub-grouped based on whether they had continuous AFib or sinus rhythm during the polysomnographic recordings. Empirical cumulative distribution functions and linear mixed models were utilized to investigate the connection between diagnosed atrial arrhythmia and the desaturation characteristics. Patients with previous atrial arrhythmia diagnosis had greater desaturation recovery area when the 100% oxygen saturation baseline reference was considered (β=0.150-0.127, p≤0.039) and more gradual recovery slopes (β=-0.181- -0.199, p<0.004) than patients without a previous atrial arrhythmia diagnosis. Furthermore, patients with AFib had more gradual oxygen saturation fall and recovery slopes than patients with sinus rhythm. Desaturation recovery characteristics in the oxygen saturation signal contains essential information about the cardiovascular response to hypoxemic periods. More comprehensive consideration of the desaturation recovery section could provide more detailed information about OSA severity, for example when developing new diagnostic parameters.

High accuracy distinction of shockable and non-shockable arrhythmias in abnormal classes through wavelet transform with pseudo differential like operators

Arrhythmia is an abnormal rhythm of the heart which leads to sudden death. Among these arrhythmias, some are shockable, and some are non-shockable arrhythmias with external defibrillation. The automated external defibrillator (AED) is used as the automated arrhythmia diagnosis system and requires an accurate and rapid decision to increase the survival rate. Therefore, a precise and quick decision by the AED has become essential in improving the survival rate. This paper presents an arrhythmia diagnosis system for the AED by engineering methods and generalized function theories. In the arrhythmia diagnosis system, the proposed wavelet transform with pseudo-differential like operators-based method effectively generates a distinguishable scalogram for the shockable and non-shockable arrhythmia in the abnormal class signals, which leads to the decision algorithm getting the best distinction. Then, a new quality parameter is introduced to get more details by quantizing the statistical features on the scalogram. Finally, design a simple AED shock and non-shock advice method by following this information to improve the precision and rapid decision. Here, an adequate topology (metric function) is adopted to the space of the scatter plot, where we can give different scales to select the best area of the scatter plot for the test sample. As a consequence, the proposed decision method gives the highest accuracy and rapid decision between shockable and non-shockable arrhythmias. The proposed arrhythmia diagnosis system increases the accuracy to 97.98%, with a gain of 11.75% compared to the conventional approach in the abnormal class signals. Therefore, the proposed method contributes an additional 11.75% possibility for increasing the survival rate. The proposed arrhythmia diagnosis system is general and could be applied to distinguish different arrhythmia-based applications. Also, each contribution could be used independently in various applications.

ECG-NET: A deep LSTM autoencoder for detecting anomalous ECG

The electrocardiogram (ECG) is a standard test to monitor the activity of the heart. Many cardiac abnormalities are manifested in the ECG including arrhythmia that refers to an abnormal heart rhythm. The basis of arrhythmia diagnosis is the identification of normal versus abnormal heart beats, and their correct classification based on ECG morphology. This paper proposes a novel and robust approach for representation learning of ECG sequences using a LSTM autoencoder for anomaly detection.The encoder part encodes the ECG signal into a lower dimensional latent space representation and decoder part then tries to reconstruct the specified ECG signal. The model is trained only on normal (non-anomalous) ECG signals. Then reconstruction loss of test ECG signals are calculated. Next a reconstruction loss threshold value is determined from the frequency distribution of the reconstruction losses so that from the reconstruction loss value above a certain threshold is determined as anomaly, otherwise it will be treated as normal. Determination of threshold is done using manual and Kapur’s automated thresholding procedures.The aforementioned model has been applied on publicly available ECG5000 dataset. From the experimental results it is observed that the proposed model achieved more than 98% accuracy having precision, recall and F1 values more than 0.94, 0.97, 0.96 respectively. The performance of the proposed method is also found to be superior in most of the cases as compared to the results of seven other recent counter-part methods reported in the literature.

An Arrhythmia Classification Model Based on Vision Transformer with Deformable Attention.

The electrocardiogram (ECG) is a highly effective non-invasive tool for monitoring heart activity and diagnosing cardiovascular diseases (CVDs). Automatic detection of arrhythmia based on ECG plays a critical role in the early prevention and diagnosis of CVDs. In recent years, numerous studies have focused on using deep learning methods to address arrhythmia classification problems. However, the transformer-based neural network in current research still has a limited performance in detecting arrhythmias for the multi-lead ECG. In this study, we propose an end-to-end multi-label arrhythmia classification model for the 12-lead ECG with varied-length recordings. Our model, called CNN-DVIT, is based on a combination of convolutional neural networks (CNNs) with depthwise separable convolution, and a vision transformer structure with deformable attention. Specifically, we introduce the spatial pyramid pooling layer to accept varied-length ECG signals. Experimental results show that our model achieved an F1 score of 82.9% in CPSC-2018. Notably, our CNN-DVIT outperforms the latest transformer-based ECG classification algorithms. Furthermore, ablation experiments reveal that the deformable multi-head attention and depthwise separable convolution are both efficient in extracting features from multi-lead ECG signals for diagnosis. The CNN-DVIT achieved good performance for the automatic arrhythmia detection of ECG signals. This indicates that our research can assist doctors in clinical ECG analysis, providing important support for the diagnosis of arrhythmia and contributing to the development of computer-aided diagnosis technology.

Safety of catheter ablation for atrial fibrillation in patients with cancer: a nationwide cohort study

ABSTRACT Background Atrial fibrillation (AF) is the most common arrhythmia in patients with cancer, especially breast, gastrointestinal, respiratory, urinary tract, and hematological malignancies. Catheter ablation (CA) is a well-established, safe treatment option in healthy patients; however, literature regarding safety of CA for AF in patients with cancer is limited and confined to single centers. Objective We aimed to assess the outcomes and peri-procedural safety of CA for AF in patients with certain types of cancer. Methods The NIS database was queried between 2016 and 2019 to identify primary hospitalizations with AF and CA. Hospitalizations with secondary diagnosis of atrial flutter and other arrhythmias were excluded. Propensity score matching was used to balance the covariates between cancer and non-cancer groups. Logistic regression was used to analyze the association. Results During this period, 47,765 CA procedures were identified, out of which 750 (1.6%) hospitalizations had a diagnosis of cancer. After propensity matching, hospitalizations with cancer diagnosis had higher in-hospital mortality (OR 3.0, 95% CI 1.5–6.2, p = 0.001), lower home discharge rates (OR 0.7, 95% CI 0.6–0.9, p < 0.001) as well as other complications such as major bleeding (OR 1.8, 95% CI 1.3–2.7, p = 0.001) and pulmonary embolism (OR 6.1, 95% CI 2.1–17.8, p < 0.001) but not associated with any major cardiac complications (OR 1.2, 95% CI 0.7–1.8, p = 0.53). Conclusion Patients with cancer who underwent CA for AF had significantly higher odds of in-hospital mortality, major bleeding, and pulmonary embolism. Further larger prospective observational studies are needed to validate these findings.

Prevalence of arrhythmia in COVID-19 patients with mild/moderate and severe illness: a prospective cohort study

ABSTRACT Objectives The type of arrhythmias, and their prevalence in mild/moderate and severe COVID-19 patients admitted to the hospital are unknown from a prospective cohort study. Methods We did continuous electrocardiograms along with multiple ECGs in 305 consecutive hospitalized COVID-19 patients. Results The incidence of arrhythmias was 6.8% (21/305) in the target population. The incidence of arrhythmias was 9.2% (17/185) in patients with severe COVID-19 illness and 3.3% (4/120) in patients with mild/moderate COVID-19 illness with no significant difference (p = 0.063). All the arrhythmias were new-onset arrhythmias in this study. 95% (20/21) of these arrhythmias were atrial arrhythmia with 71.42% (15/21) being atrial fibrillation and one episode of sustained polymorphic ventricular tachycardia. No episode of high-grade atrioventricular block, sustained monomorphic ventricular arrhythmia, or torsades de pointes arrhythmias were observed in this study. The patients with arrhythmias were admitted to the intensive care unit (80.9% vs. 50.7%; p: 0.007), were on a ventilator (47.6% vs. 21.4%; p: 0.006), and had high in-hospital mortality (57.1% vs. 21.1%; p: 0.0001) than patients without arrhythmias. Conclusion Atrial arrhythmias were the most frequent arrhythmias in hospital-admitted COVID-19 patients with atrial fibrillation being the most common arrhythmia. Trial registration Clinical Trial Registry India (CTRI) (CTRI/2021/01/030788). (https://www.ctri.nic.in/)

NSICA: Multi-objective imperialist competitive algorithm for feature selection in arrhythmia diagnosis

This study proposes a multi-objective, non-dominated, imperialist competitive algorithm (NSICA) to solve optimal feature selection problems. The NSICA is a multi-objective and discrete version of the original Imperialist Competitive Algorithm (ICA) that utilizes the competition between colonies and imperialists to solve optimization problems. This study focused on solving challenges such as discretization and elitism by modifying the original operations and using a non-dominated sorting approach. The proposed algorithm is independent of the application, and with customization, it could be employed to solve any feature selection problem. We evaluated the algorithm's efficiency using it as a feature selection system for diagnosing cardiac arrhythmias. The Pareto optimal selected features from NSICA were utilized to classify arrhythmias in binary and multi-class forms based on three essential objectives: accuracy, number of features, and false negativity. We applied NSICA to an ECG-based arrhythmia classification dataset from the UCI machine learning repository. The evaluation results indicate the efficiency of the proposed algorithm compared to other state-of-the-art algorithms.

Interim evaluation of insertable cardiac monitor signal quality in the LUX-Dx PERFORM study

Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): Boston Scientific Corporation Background The LUX-Dx PERFORM Study is designed to evaluate safety and performance of the LUX-Dx Insertable Cardiac Monitor (ICM) in a general patient population. Although follow-up is ongoing, enrollment is complete, and at least half of the patients have completed 180 days of follow-up. The visibility (or absence) of P-waves in the ICM-recorded S-ECG is an important feature in the diagnosis of atrial arrhythmias. Objective Evaluate the visibility of P-waves in patients enrolled in the LUX-Dx PERFORM study. Methods P-wave visibility was evaluated from the first 356 patients enrolled in the LUX-Dx PERFORM study. The daily presenting S-ECGs (10 second rhythm) nearest to Day 7 (+/- 1) and Day 180 (+/- 7) after implant were blindly adjudicated by independent electrophysiologists to calculate the percentage of beats with clearly visible P-waves. Results Of 636 reviewed S-ECGs, 24 (3.7%) were excluded from analysis for presence of AF rhythm (11), or unavailable data (8 with insufficient adjudication and 5 with no detected R-waves from which to calculate the percentage of beats with visible P-waves). Figure 1 shows an example of a presenting S-ECG rhythm with clearly visible P-waves from the patient management system LATITUDE Clarity. The P-waves were found to be clearly visible in 89.1% of the total heart cycles (Day 7: 90.4%, Day 180: 87.8%). The average difference in percent visibility between Day 7 and Day 180 was approximately 2.2% (p=.248). At a patient level, 88% of patients had visible P-waves in at least one presenting ECG for more than 90% of beats, as shown in Figure 2. In addition, 69% of patients had visible P-waves in both presenting ECGs (Day 7 and Day 180) for at least 90% of beats. Conclusion The LUX-Dx showed consistent visibility of P-waves in the first half of patients enrolled in the LUX-Dx PERFORM study, which represents a general patient population. Adequate P-wave visibility is an important clinical feature in diagnosing atrial arrhythmias and may aid in analysis and clinic workflow efficiency.

Does optimized daytime/nighttime programming reduce insertable cardiac monitor event burden?

Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): Boston Scientific. Background Insertable Cardiac Monitors (ICM) are widely used for long term cardiac arrhythmia monitoring and diagnosis. ICMs can generate episodes (and associated alerts) that are non-actionable from a clinical standpoint leading to alert fatigue. In particular, non-actionable bradycardia and pause events may occur at nighttime due to reduced sympathetic tone during sleep. We have used stored auto-detected events to better understand optimal programming with nighttime settings to eliminate potentially clinically non-actionable episodes and improve clinic workflow. Objective Evaluate the reduction in clinic episode review burden in bradycardia (brady) and pause episodes with a new daytime/nighttime programming optimization in LUX-Dx ICM. Methods We analyzed data from 2,555 patients implanted with LUX-Dx ICM that included 132,928 detected brady and pause episodes stored remotely. We evaluated optimized settings with separately programmed nocturnal (11p-7a) and nominal daytime (7a-11p) rates and durations as compared to full-time nominal settings (24-hour nominal). Optimized nocturnal programming was set to trigger event storage on a heart rate setting of ≤30 bpm as well as a duration setting of ≥5s for nocturnal brady and pause episodes. The nominal day time and 24-hour settings for brady and pause episodes were HR ≤40bpm and duration ≥3s. The episode burden was analyzed retrospectively using stored episodes comparing optimized versus full-time nominal programming. Results A simulation using the optimized settings showed that 98% and 90% of the nocturnal bradycardia (n = 1909) and pause episodes (n = 1198) had a HR &amp;gt;30bpm, and duration &amp;lt;5s respectively. As a result, the optimized settings for nocturnal episodes reduced 75% and 56% of bradycardia and pause episodes, respectively. (See Figure 1). At a patient level, 63% and 25% of patients had a substantial reduction (&amp;gt;=50%) in brady and pause episodes, respectively. Conclusion The optimized programming settings, which simply adjusts for nocturnal physiology, may substantially improve clinical workflow by reducing potentially clinically non-actionable episodes and associated alerts detected by the LUX-Dx ICM. More clinical correlative studies are needed to understand patient risk.