An open-access simultaneous electrocardiogram and phonocardiogram database

Objective.The EPHNOGRAM project aimed to develop a low-cost, low-power device for simultaneous electrocardiogram (ECG) and phonocardiogram (PCG) recording, with additional channels for environmental audio to enhance PCG through active noise cancellation. The objective was to study multimodal electro-mechanical activities of the heart, offering insights into the differences and synergies between these modalities during various cardiac activity levels.Approach.We developed and tested several hardware prototypes of a simultaneous ECG-PCG acquisition device. Using this technology, we collected simultaneous ECG and PCG data from 24 healthy adults during different physical activities, including resting, walking, running, and stationary biking, in an indoor fitness center. The data were annotated using a robust software that we developed for detecting ECG R-peaks and PCG S1 and S2 components, and overseen by a human expert. We also developed machine learning models using ECG-based, PCG-based, and joint ECG-PCG features, like R-R and S1-S2 intervals, to classify physical activities and analyze electro-mechanical dynamics.Main results.The results show a significant coupling between ECG and PCG components, especially during high-intensity exercise. Notable micro-variations in S2-based heart rate show differences in the heart's electrical and mechanical functions. The Lomb-Scargle periodogram and approximate entropy analyses confirm the higher volatility of S2-based heart rate compared to ECG-based heart rate. Correlation analysis shows stronger coupling between R-R and R-S1 intervals during high-intensity activities. Hybrid ECG-PCG features, like the R-S2 interval, were identified as more informative for physical activity classification through mRMR feature selection and SHAP value analysis.Significance.The EPHNOGRAM database, is available on PhysioNet. The database enhances our understanding of cardiac function, enabling future studies on the heart's mechanical and electrical interrelationships. The results of this study can contribute to improved cardiac condition diagnoses. Additionally, the designed hardware has the potential for integration into wearable devices and the development of multimodal stress test technologies.

Examination of Cardiac Activity with ECG Monitoring Using Heart Rate Variability Methods

The paper presents a system for analyzing cardiac activity with the possibility of continuous and remote monitoring. The created sensor mobile device monitors heart activity by means of the convenient and imperceptible registration of cardiac signals. At the same time, the behavior of the human body is also monitored through the accelerometer and gyroscope built into the device, thanks to which it is possible to signal in the event of loss of consciousness or fall (in patients with syncope). Conducting real-time cardio monitoring and the analysis of recordings using various mathematical methods (linear, non-linear, and graphical) enables the research, accurate diagnosis, timely assistance, and correct treatment of cardiovascular diseases. The paper examines the recordings of patients diagnosed with arrhythmia and syncope recorded by electrocardiography (ECG) sensors in real conditions. The obtained results are subjected to statistical analysis to determine the accuracy and significance of the obtained results. The studies show significant deviations in the patients with arrhythmia and syncope regarding the obtained values of the studied parameters of heart rate variability (HRV) from the accepted normal values (for example, the root mean square of successive differences between normal heartbeats (RMSSD) in healthy individuals is 24.02 ms, while, in patients with arrhythmia (6.09 ms) and syncope (5.21 ms), it is much lower). The obtained quantitative and graphic results identify some possible abnormalities and demonstrate disorders regarding the activity of the autonomic nervous system, which is directly related to the work of the heart.

Biodegradable, Biocompatible, and Implantable Multifunctional Sensing Platform for Cardiac Monitoring

Cardiac monitoring after heart surgeries is crucial for health maintenance and detecting postoperative complications early. However, current methods like rigid implants have limitations, as they require performing second complex surgeries for removal, increasing infection and inflammation risks, thus prompting research for improved sensing monitoring technologies. Herein, we introduce a nanosensor platform that is biodegradable, biocompatible, and integrated with multifunctions, suitable for use as implants for cardiac monitoring. The device has two electrochemical biosensors for sensing lactic acid and pH as well as a pressure sensor and a chemiresistor array for detecting volatile organic compounds. Its biocompatibility with myocytes has been tested in vitro, and its biodegradability and sensing function have been proven with ex vivo experiments using a three-dimensional (3D)-printed heart model and 3D-printed cardiac tissue patches. Moreover, an artificial intelligence-based predictive model was designed to fuse sensor data for more precise health assessment, making it a suitable candidate for clinical use. This sensing platform promises impactful applications in the realm of cardiac patient care, laying the foundation for advanced life-saving developments.

Corrigendum: Estimation of left ventricular end-systolic elastance from brachial pressure waveform via deep learning

[This corrects the article DOI: 10.3389/fbioe.2021.754003.].

Reversibility of Sinus Bradycardia-Induced Syncope Resulting From Low-Voltage Electrical Injury: A Case Report

Electric shocks pose a serious threat to public health. The heart is among the organs that are most commonly impacted. Electrical harm can cause a number of potentially fatal heart conditions, including asystole, ventricular fibrillation, and myocardial rupture. Some patients had sinus bradycardia diagnosed at the time of admission. In this case report, we describe a 43-year-old male patient who had an electrical injury that resulted in syncope and sinus bradycardia. After 24 hours of cardiac monitoring, the patient was found to not require a pacemaker. This suggests that patients with symptomatic sinus bradycardia should have cardiac monitoring. If, after 24 hours, cardiac monitoring revealed no new episodes of sinus bradycardia and the patient remained asymptomatic, the patient is unlikely to require a pacemaker. There are differing guidelines and suggestions regarding the supervision of patients following electrical damage, and further study in this area is necessary to enable the unification of guidelines.

Arrhythmias in patients with X-linked myotubular myopathy

INTRODUCTION

Myotubular myopathy is a congenital muscle disease caused by a mutation in the myotubularin (MTM1) gene. The X-linked myotubular myopathy (XLMTM) affects males with early-onset symptoms such as muscle weakness, hypotonia, and respiratory distress. To our knowledge, cardiac involvement has not been previously described in this condition, in contrast to other types of congenital myopathies such as nemaline myopathy or core myopathy.

CASE REPORTS

We report two clinical cases of XLMTM that started with severe sinus bradycardia or auriculoventricular block from the first days of life, with pathologic 24-hours Holter monitoring in both cases. A primary cardiac affection was excluded by electrophysiological studies and normal heart rate was recovered with proper respiratory support.

DISCUSSION

These cases with sever bradyarrhythmia in a well know pathology such the XLMTM represents a nuance on the usual differential diagnostics of congenital myopathies.

Continuous Data-Driven Monitoring in Critical Congenital Heart Disease: Clinical Deterioration Model Development

BACKGROUND

Critical congenital heart disease (cCHD)-requiring cardiac intervention in the first year of life for survival-occurs globally in 2-3 of every 1000 live births. In the critical perioperative period, intensive multimodal monitoring at a pediatric intensive care unit (PICU) is warranted, as their organs-especially the brain-may be severely injured due to hemodynamic and respiratory events. These 24/7 clinical data streams yield large quantities of high-frequency data, which are challenging in terms of interpretation due to the varying and dynamic physiology innate to cCHD. Through advanced data science algorithms, these dynamic data can be condensed into comprehensible information, reducing the cognitive load on the medical team and providing data-driven monitoring support through automated detection of clinical deterioration, which may facilitate timely intervention.

OBJECTIVE

This study aimed to develop a clinical deterioration detection algorithm for PICU patients with cCHD.

METHODS

Retrospectively, synchronous per-second data of cerebral regional oxygen saturation (rSO₂) and 4 vital parameters (respiratory rate, heart rate, oxygen saturation, and invasive mean blood pressure) in neonates with cCHD admitted to the University Medical Center Utrecht, the Netherlands, between 2002 and 2018 were extracted. Patients were stratified based on mean oxygen saturation during admission to account for physiological differences between acyanotic and cyanotic cCHD. Each subset was used to train our algorithm in classifying data as either stable, unstable, or sensor dysfunction. The algorithm was designed to detect combinations of parameters abnormal to the stratified subpopulation and significant deviations from the patient's unique baseline, which were further analyzed to distinguish clinical improvement from deterioration. Novel data were used for testing, visualized in detail, and internally validated by pediatric intensivists.

RESULTS

A retrospective query yielded 4600 hours and 209 hours of per-second data in 78 and 10 neonates for, respectively, training and testing purposes. During testing, stable episodes occurred 153 times, of which 134 (88%) were correctly detected. Unstable episodes were correctly noted in 46 of 57 (81%) observed episodes. Twelve expert-confirmed unstable episodes were missed in testing. Time-percentual accuracy was 93% and 77% for, respectively, stable and unstable episodes. A total of 138 sensorial dysfunctions were detected, of which 130 (94%) were correct.

CONCLUSIONS

In this proof-of-concept study, a clinical deterioration detection algorithm was developed and retrospectively evaluated to classify clinical stability and instability, achieving reasonable performance considering the heterogeneous population of neonates with cCHD. Combined analysis of baseline (ie, patient-specific) deviations and simultaneous parameter-shifting (ie, population-specific) proofs would be promising with respect to enhancing applicability to heterogeneous critically ill pediatric populations. After prospective validation, the current-and comparable-models may, in the future, be used in the automated detection of clinical deterioration and eventually provide data-driven monitoring support to the medical team, allowing for timely intervention.

Corrigendum: How often is occult atrial fibrillation in cryptogenic stroke causal vs. incidental? A meta-analysis

[This corrects the article DOI: 10.3389/fneur.2023.1103664.].

First Ex Vivo Animal Study of a Biological Heart Valve Prosthesis Sensorized with Intravalvular Impedance

IntraValvular Impedance (IVI) sensing is an innovative concept for monitoring heart valve prostheses after implant. We recently demonstrated IVI sensing feasible in vitro for biological heart valves (BHVs). In this study, for the first time, we investigate ex vivo the IVI sensing applied to a BHV when it is surrounded by biological tissue, similar to a real implant condition. A commercial model of BHV was sensorized with three miniaturized electrodes embedded in the commissures of the valve leaflets and connected to an external impedance measurement unit. To perform ex vivo animal tests, the sensorized BHV was implanted in the aortic position of an explanted porcine heart, which was connected to a cardiac BioSimulator platform. The IVI signal was recorded in different dynamic cardiac conditions reproduced with the BioSimulator, varying the cardiac cycle rate and the stroke volume. For each condition, the maximum percent variation in the IVI signal was evaluated and compared. The IVI signal was also processed to calculate its first derivative (dIVI/dt), which should reflect the rate of the valve leaflets opening/closing. The results demonstrated that the IVI signal is well detectable when the sensorized BHV is surrounded by biological tissue, maintaining the similar increasing/decreasing trend that was found during in vitro experiments. The signal can also be informative on the rate of valve opening/closing, as indicated by the changes in dIVI/dt in different dynamic cardiac conditions.

Postural and longitudinal variability in seismocardiographic signals

Objective. Low frequency cardiovascular vibrations detectable on the chest surface (termed seismocardiography or SCG) may be useful for non-invasive diagnosis and monitoring of various cardiovascular conditions. A potential limitation of using SCG for longitudinal patient monitoring is the existence of intra-subject variability, which can contribute to errors in calculating SCG features. Improved understanding of the contribution of intra-subject variability sources may lead to improved SCG utility. This study aims to quantify postural and longitudinal SCG variability in healthy resting subjects during normal breathing.Approach. SCG and ECG signals were longitudinally acquired in 19 healthy subjects at different postures (supine, 45° head up, and sitting) during five recording sessions over five months. SCG cycles were segmented using the ECG R wave. Unsupervised machine learning was used to reduce SCG variability due to respiration by grouping the SCG signals into two clusters with minimized intra-cluster waveform heterogeneity. Several SCG features were assessed at different postures and longitudinally.Main results. SCG waveform morphological variability was calculated within each cluster (intra-cluster) and between two clusters (inter-cluster) at each posture and data collection session. The variabilities were significantly different between the supine and sitting but not between supine and 45° postures. For the 45° and sitting postures, the intra-cluster variability was not significantly different, while the inter-cluster variability difference was significant. The energy ratio between different frequency bands to total spectral energy in 0.5-50 Hz were calculated and were comparable for all postures. The combined cardiac timing intervals from the two clusters showed significant variation with postural changes. There was significant heart rate difference between the clusters and between postural positions. The SCG features were compared between longitudinal sessions and all features were not significantly different,Significance. Several SCG features significantly varied with posture suggesting that posture needs to be specified when comparing SCG changes over time. Longitudinally comparable SCG feature values suggests that significant longitudinal differences, if observed, may reflect true alternations in the cardiac functioning over time.

Association of Chronic Covert Cerebral Infarctions and White Matter Hyperintensities With Atrial Fibrillation Detection on Post-Stroke Cardiac Rhythm Monitoring: A Cohort Study

Background This study was conducted to explore the association of different phenotypes, count, and location of chronic covert brain infarctions (CBIs) with detection of atrial fibrillation (AF) on prolonged post-stroke cardiac rhythm monitoring (PCM). Methods and Results We conducted a cohort single-center study of consecutive first-ever ischemic stroke or transient ischemic attack patients undergoing PCM between January 2015 and December 2017. We blindly rated CBI phenotypes according to established definitions and white matter hyperintensities (WMHs) according to the age-related white matter changes rating scale. We used (multiple) regression models to assess the association of the imaging biomarkers and incident AF on PCM. A total of 795 patients (median [interquartile range]) aged 69 (57-78) years, 41% women, median National Institutes of Health Stroke Scale score 2 (0-5), median PCM duration 14 (7-14) days, and AF detection in 61 patients (7.7%) were included. On univariate analysis, WMHs (per point odds ratio, 1.35 [95% CI, 1.03-1.78]) but not CBIs (odds ratio, 0.90 [95% CI, 0.52-1.56]) were associated with AF detection. Neither CBI phenotype, count, nor location were associated with AF detection. After adjustment for age, hypertension, and stroke severity, neither increasing WMHs (per point adjusted odds ratio, 0.85 [95% CI, 0.60-1.20]) nor CBIs (adjusted odds ratio, 0.60 [95% CI, 0.33-1.09]) were independently associated with AF detection. Conclusions Although WMHs and CBIs represent surrogate biomarkers of vascular risk factors, neither WMHs nor CBIs, including their phenotypes, count, and location, were independently associated with AF detection on PCM. In patients with manifest ischemic stroke or transient ischemic attack, the presence of imaging biomarkers of chronic ischemic injury does not seem promising to further refine prediction tools for AF detection on PCM.

Changes in Forcecardiography Heartbeat Morphology Induced by Cardio-Respiratory Interactions

The cardiac function is influenced by respiration. In particular, various parameters such as cardiac time intervals and the stroke volume are modulated by respiratory activity. It has long been recognized that cardio-respiratory interactions modify the morphology of cardio-mechanical signals, e.g., phonocardiogram, seismocardiogram (SCG), and ballistocardiogram. Forcecardiography (FCG) records the weak forces induced on the chest wall by the mechanical activity of the heart and lungs and relies on specific force sensors that are capable of monitoring respiration, infrasonic cardiac vibrations, and heart sounds, all simultaneously from a single site on the chest. This study addressed the changes in FCG heartbeat morphology caused by respiration. Two respiratory-modulated parameters were considered, namely the left ventricular ejection time (LVET) and a morphological similarity index (MSi) between heartbeats. The time trends of these parameters were extracted from FCG signals and further analyzed to evaluate their consistency within the respiratory cycle in order to assess their relationship with the breathing activity. The respiratory acts were localized in the time trends of the LVET and MSi and compared with a reference respiratory signal by computing the sensitivity and positive predictive value (PPV). In addition, the agreement between the inter-breath intervals estimated from the LVET and MSi and those estimated from the reference respiratory signal was assessed via linear regression and Bland-Altman analyses. The results of this study clearly showed a tight relationship between the respiratory activity and the considered respiratory-modulated parameters. Both the LVET and MSi exhibited cyclic time trends that remarkably matched the reference respiratory signal. In addition, they achieved a very high sensitivity and PPV (LVET: 94.7% and 95.7%, respectively; MSi: 99.3% and 95.3%, respectively). The linear regression analysis reported almost unit slopes for both the LVET (R² = 0.86) and MSi (R² = 0.97); the Bland-Altman analysis reported a non-significant bias for both the LVET and MSi as well as limits of agreement of ±1.68 s and ±0.771 s, respectively. In summary, the results obtained were substantially in line with previous findings on SCG signals, adding to the evidence that FCG and SCG signals share a similar information content.

Catecholamine-induced acute myocardial stunning after accidental intra-operative noradrenaline bolus

We report a case of catecholamine-induced acute myocardial stunning that occurred in a six-year-old girl. This was triggered by an accidental noradrenaline injection during general anaesthesia for dental surgery. The clinical course was favourable, although cardiac enzymes and echocardiography were significantly altered. The child was discharged home on the second postoperative day, after complete clinical resolution. We emphasise the need to consider shortening the surgical procedure, and to closely monitor patients following a medication error involving vasopressors even in the absence of symptoms. We highlight the importance of a controlled process for storing, identifying, preparing, and handling medications. The identification of weaknesses in the overall process of drug prescription and administration is of utmost importance.

Cardiac Rhythm Monitoring Using Wearables for Clinical Guidance before and after Catheter Ablation

Mobile health technologies are gaining importance in clinical decision-making. With the capability to monitor the patient’s heart rhythm, they have the potential to reduce the time to confirm a diagnosis and therefore are useful in patients eligible for screening of atrial fibrillation as well as in patients with symptoms without documented symptom rhythm correlation. Such is crucial to enable an adequate arrhythmia management including the possibility of a catheter ablation. After ablation, wearables can help to search for recurrences, in symptomatic as well as in asymptomatic patients. Furthermore, those devices can be used to search for concomitant arrhythmias and have the potential to help improving the short- and long-term patient management. The type of wearable as well as the adequate technology has to be chosen carefully for every situation and every individual patient, keeping different aspects in mind. This review aims to describe and to elaborate a potential workflow for the role of wearables for cardiac rhythm monitoring regarding detection and management of arrhythmias before and after cardiac electrophysiological procedures.

A Comparison of Heart Pulsations Provided by Forcecardiography and Double Integration of Seismocardiogram

Seismocardiography (SCG) is largely regarded as the state-of-the-art technique for continuous, long-term monitoring of cardiac mechanical activity in wearable applications. SCG signals are acquired via small, lightweight accelerometers fixed on the chest. They provide timings of important cardiac events, such as heart valves openings and closures, thus allowing the estimation of cardiac time intervals of clinical relevance. Forcecardiography (FCG) is a novel technique that records the cardiac-induced vibrations of the chest wall by means of specific force sensors, which proved capable of monitoring respiration, heart sounds and infrasonic cardiac vibrations, simultaneously from a single contact point on the chest. A specific infrasonic component captures the heart walls displacements and looks very similar to the Apexcardiogram. This low-frequency component is not visible in SCG recordings, nor it can be extracted by simple filtering. In this study, a feasible way to extract this information from SCG signals is presented. The proposed approach is based on double integration of SCG. Numerical double integration is usually very prone to large errors, therefore a specific numerical procedure was devised. This procedure yields a new displacement signal (DSCG) that features a low-frequency component (LF-DSCG) very similar to that of the FCG (LF-FCG). Experimental tests were carried out using an FCG sensor and an off-the-shelf accelerometer firmly attached to each other and placed onto the precordial region. Simultaneous recordings were acquired from both sensors, together with an electrocardiogram lead (used as a reference). Quantitative morphological comparison confirmed the high similarity between LF-FCG and LF-DSCG (normalized cross-correlation index >0.9). Statistical analyses suggested that LF-DSCG, although achieving a fair sensitivity in heartbeat detection (about 90%), has not a very high consistency within the cardiac cycle, leading to inaccuracies in inter-beat intervals estimation. Future experiments with high-performance accelerometers and improved processing methods are envisioned to investigate the potential enhancement of the accuracy and reliability of the proposed method.

Detection of Aortic Valve Opening and Estimation of Pre-Ejection Period in Forcecardiography Recordings

Forcecardiography (FCG) is a novel technique that measures the local forces induced on the chest wall by the mechanical activity of the heart. Specific piezoresistive or piezoelectric force sensors are placed on subjects’ thorax to measure these very small forces. The FCG signal can be divided into three components: low-frequency FCG, high-frequency FCG (HF-FCG) and heart sound FCG. HF-FCG has been shown to share a high similarity with the Seismocardiogram (SCG), which is commonly acquired via small accelerometers and is mainly used to locate specific fiducial markers corresponding to essential events of the cardiac cycle (e.g., heart valves opening and closure, peaks of blood flow). However, HF-FCG has not yet been demonstrated to provide the timings of these markers with reasonable accuracy. This study addresses the detection of the aortic valve opening (AO) marker in FCG signals. To this aim, simultaneous recordings from FCG and SCG sensors were acquired, together with Electrocardiogram (ECG) recordings, from a few healthy subjects at rest, both during quiet breathing and apnea. The AO markers were located in both SCG and FCG signals to obtain pre-ejection periods (PEP) estimates, which were compared via statistical analyses. The PEPs estimated from FCG and SCG showed a strong linear relationship (r > 0.95) with a practically unit slope, and 95% of their differences were found to be distributed within ± 4.6 ms around small biases of approximately 1 ms, corresponding to percentage differences lower than 5% of the mean measured PEP. These preliminary results suggest that FCG can provide accurate AO timings and PEP estimates.

Heart Rate Variability Analysis to Evaluate Autonomic Nervous System Maturation in Neonates: An Expert Opinion

While heart rate variability (HRV) is a relevant non-invasive tool to assess the autonomic nervous system (ANS) functioning with recognized diagnostic and therapeutic implications, the lack of knowledge on its interest in neonatal medicine is certain. This review aims to briefly describe the algorithms used to decompose variations in the length of the RR interval and better understand the physiological autonomic maturation data of the newborn. Assessing newborns' autonomous reactivity can identify dysautonomia situations and discriminate children with a high risk of life-threatening events, which should benefit from cardiorespiratory monitoring at home. Targeted monitoring of HRV should provide an objective reflection of the newborn's intrinsic capacity for cardiorespiratory self-regulation.

A Randomized Trial Comparing 3- versus 4-Monthly Cardiac Monitoring in Patients Receiving Trastuzumab-Based Chemotherapy for Early Breast Cancer

PURPOSE

The optimal frequency for cardiac monitoring of left ventricular ejection fraction (LVEF) in patients receiving trastuzumab-based therapy for early breast cancer (EBC) is unknown. We conducted a randomized controlled trial comparing 3- versus 4-monthly cardiac monitoring.

PATIENTS AND METHOD

Patients scheduled to receive trastuzumab-containing cancer therapy for EBC with normal (>53%) baseline LVEF were randomized to undergo LVEF assessments every 3 or 4 months. The primary outcome was the change in LVEF from baseline. Secondary outcomes included the rate of cardiac dysfunction (defined as a decrease in the LVEF of ≥10 percentage points, to a value <53%), delays in or discontinuation of trastuzumab therapy, and cardiology referral.

RESULTS

Of the 200 eligible and enrolled patients, 100 (50%) were randomized to 3-monthly and 100 (50%) to 4-monthly cardiac monitoring. Of these patients, 98 and 97 respectively underwent at least one cardiac scan. The estimated mean difference in LVEF from baseline was -0.94% (one-sided 95% lower bound: -2.14), which exceeded the pre-defined non-inferiority margin of -4%. There were also no significant differences between the two study arms for any of the secondary endpoints. The rate of detection of cardiac dysfunction was 16.3% (16/98) and 12.4% (12/97) in the 3- and 4-monthly arms, respectively (95% CI: 4.0 [-5.9, 13.8]).

CONCLUSIONS

Cardiac monitoring every 4 months was deemed non-inferior to that every 3 months in patients with HER2-positive EBC being treated with trastuzumab-based therapy. Given its costs and inconvenience, cardiac monitoring every 4 months should be considered standard practice. Registration: NCT02696707, 18 February 2016.

Estimation of Left Ventricular End-Systolic Elastance From Brachial Pressure Waveform via Deep Learning

Determination of left ventricular (LV) end-systolic elastance (E es ) is of utmost importance for assessing the cardiac systolic function and hemodynamical state in humans. Yet, the clinical use of E es is not established due to the invasive nature and high costs of the existing measuring techniques. The objective of this study is to introduce a method to assess cardiac contractility, using as a sole measurement an arterial blood pressure (BP) waveform. Particularly, we aim to provide evidence on the potential in using the morphology of the brachial BP waveform and its time derivative for predicting LV E es via convolution neural networks (CNNs). The requirement of a broad training dataset is addressed by the use of an in silico dataset (n = 3,748) which is generated by a validated one-dimensional mathematical model of the cardiovasculature. We evaluated two CNN configurations: 1) a one-channel CNN (CNN1) with only the raw brachial BP signal as an input, and 2) a two-channel CNN (CNN2) using as inputs both the brachial BP wave and its time derivative. Accurate predictions were yielded using both CNN configurations. For CNN1, Pearson's correlation coefficient (r) and RMSE were equal to 0.86 and 0.27 mmHg/ml, respectively. The performance was found to be greatly improved for CNN2 (r = 0.97 and RMSE = 0.13 mmHg/ml). Moreover, all absolute errors from CNN2 were found to be less than 0.5 mmHg/ml. Importantly, the brachial BP wave appeared to be a promising source of information for estimating E es . Predictions were found to be in good agreement with the reference E es values over an extensive range of LV contractility values and loading conditions. Therefore, the proposed methodology could be easily transferred to the bedside and potentially facilitate the clinical use of E es for monitoring the contractile state of the heart in the real-life setting.

Cardiotoxicity and Cardiac Monitoring Among Anthracycline-Treated Cancer Patients: A Retrospective Cohort Study

Purpose

Cardiotoxicity is a common complication associated with anthracyclines. Little is known regarding the rate of anthracyclines-related acute and chronic cardiotoxicity and adherence to cardiac monitoring recommendations among cancer patients.

Patients and Methods

A single-centre retrospective cohort study was conducted from 2015 to 2018 on patients with cancer, 18 years of age and older, on anthracyclines without a history of cardiovascular diseases. Data on demographic information, comorbidities, cardiovascular events, monitoring parameters, and treatment details were obtained. The primary outcome was the incidence of anthracyclines-related cardiotoxicity both acute and chronic. The secondary outcome was to determine adherence to guideline recommendations for monitoring anthracyclines-related cardiotoxicity based on the American Society of Clinical Oncology clinical practice guidelines. Analyses included descriptive statistics and logistic regression. Institutional review board approval was obtained.

Results

In 235 patients identified, 28.9% developed cardiotoxicity, of which 27.2% were acute, while chronic cardiotoxicity was observed in 8.9% of subjects. Patients who received optimal cardiac monitoring had a statistically significant higher odds of developing cardiotoxicities (odds ratio=2.65, confidence interval=1.32-5.33). The risk of cardiotoxicity was higher in subjects with a history of diabetes mellitus, those using daunorubicin, and concomitant filgrastim use. Adherence to guideline recommendations was only achieved in 25.1% of the population. Echocardiography was the most common monitoring method used.

Conclusion

In this study, there was a high incidence of anthracyclines cardiotoxicity and poor compliance with cardiac monitoring recommendations for cancer patients on anthracyclines, which underscores acute and chronic cardiotoxicity in this population.

Tensor-Based ECG Anomaly Detection toward Cardiac Monitoring in the Internet of Health Things

Advanced heart monitors, especially those enabled by the Internet of Health Things (IoHT), provide a great opportunity for continuous collection of the electrocardiogram (ECG), which contains rich information about underlying cardiac conditions. Realizing the full potential of IoHT-enabled cardiac monitoring hinges, to a great extent, on the detection of disease-induced anomalies from collected ECGs. However, challenges exist in the current literature for IoHT-based cardiac monitoring: (1) Most existing methods are based on supervised learning, which requires both normal and abnormal samples for training. This is impractical as it is generally unknown when and what kind of anomalies will occur during cardiac monitoring. (2) Furthermore, it is difficult to leverage advanced machine learning approaches for information processing of 1D ECG signals, as most of them are designed for 2D images and higher-dimensional data. To address these challenges, a new sensor-based unsupervised framework is developed for IoHT-based cardiac monitoring. First, a high-dimensional tensor is generated from the multi-channel ECG signals through the Gramian Angular Difference Field (GADF). Then, multi-linear principal component analysis (MPCA) is employed to unfold the ECG tensor and delineate the disease-altered patterns. Obtained principal components are used as features for anomaly detection using machine learning models (e.g., deep support vector data description (deep SVDD)) as well as statistical control charts (e.g., Hotelling T2 chart). The developed framework is evaluated and validated using real-world ECG datasets. Comparing to the state-of-the-art approaches, the developed framework with deep SVDD achieves superior performances in detecting abnormal ECG patterns induced by various types of cardiac disease, e.g., an F-score of 0.9771 is achieved for detecting atrial fibrillation, 0.9986 for detecting right bundle branch block, and 0.9550 for detecting ST-depression. Additionally, the developed framework with the T² control chart facilitates personalized cycle-to-cycle monitoring with timely detected abnormal ECG patterns. The developed framework has a great potential to be implemented in IoHT-enabled cardiac monitoring and smart management of cardiac health.

Quality Improvement in Atrial Fibrillation detection after ischaemic stroke (QUIT-AF)

BACKGROUND

Paroxysmal atrial fibrillation (PAF) is a frequent cause of recurrent stroke but can be difficult to detect because of its episodic and often asymptomatic nature. We sought to improve rate of PAF detection through a quality improvement project (QIP) to deliver early prolonged inpatient cardiac monitoring on the stroke unit (SU).

METHODS

A structured protocol for cardiac monitoring using 5-day event recorders was established. 'In-house' cardiac monitoring was implemented. Performance data on this change in service was analysed prospectively and summary statistics obtained.

RESULTS

One-hundred and two ischaemic stroke (IS) patients undertook 5-day event recorder monitoring. Provision of monitors as an inpatient (IP) increased from 20% (pre-QIP pilot 2018) to 65.7% (during QIP). New AF was detected in 15 patients (14.7% vs 8.6% pre-QIP pilot 2018) with majority of new AF (13 patients; 19%) detected when monitors applied early (IP) after IS.

CONCLUSION

Although this study had a number of limitations, it did demonstrate that early and prolonged non-invasive IP cardiac monitoring could be delivered 'in-house' on the SU and improve AF detection rates.

AI-Based Estimation of End-Systolic Elastance From Arm-Pressure and Systolic Time Intervals

Left ventricular end-systolic elastance (E_es) is a major determinant of cardiac systolic function and ventricular-arterial interaction. Previous methods for the E_es estimation require the use of the echocardiographic ejection fraction (EF). However, given that EF expresses the stroke volume as a fraction of end-diastolic volume (EDV), accurate interpretation of EF is attainable only with the additional measurement of EDV. Hence, there is still need for a simple, reliable, noninvasive method to estimate E_es. This study proposes a novel artificial intelligence—based approach to estimate E_es using the information embedded in clinically relevant systolic time intervals, namely the pre-ejection period (PEP) and ejection time (ET). We developed a training/testing scheme using virtual subjects (n = 4,645) from a previously validated in-silico model. Extreme Gradient Boosting regressor was employed to model E_es using as inputs arm cuff pressure, PEP, and ET. Results showed that E_es can be predicted with high accuracy achieving a normalized RMSE equal to 9.15% (r = 0.92) for a wide range of E_es values from 1.2 to 4.5 mmHg/ml. The proposed model was found to be less sensitive to measurement errors (±10–30% of the actual value) in blood pressure, presenting low test errors for the different levels of noise (RMSE did not exceed 0.32 mmHg/ml). In contrast, a high sensitivity was reported for measurements errors in the systolic timing features. It was demonstrated that E_es can be reliably estimated from the traditional arm-pressure and echocardiographic PEP and ET. This approach constitutes a step towards the development of an easy and clinically applicable method for assessing left ventricular systolic function.

Smart Wearables for Cardiac Monitoring-Real-World Use beyond Atrial Fibrillation

The possibilities and implementation of wearable cardiac monitoring beyond atrial fibrillation are increasing continuously. This review focuses on the real-world use and evolution of these devices for other arrhythmias, cardiovascular diseases and some of their risk factors beyond atrial fibrillation. The management of nonatrial fibrillation arrhythmias represents a broad field of wearable technologies in cardiology using Holter, event recorder, electrocardiogram (ECG) patches, wristbands and textiles. Implementation in other patient cohorts, such as ST-elevation myocardial infarction (STEMI), heart failure or sleep apnea, is feasible and expanding. In addition to appropriate accuracy, clinical studies must address the validation of clinical pathways including the appropriate device and clinical decisions resulting from the surrogate assessed.

Quantification of Cardiac Kinetic Energy and Its Changes During Transmural Myocardial Infarction Assessed by Multi-Dimensional Seismocardiography

Introduction: Seismocardiography (SCG) records cardiac and blood-induced motions transmitted to the chest surface as vibratory phenomena. Evidences demonstrate that acute myocardial ischemia (AMI) profoundly affects the SCG signals. Multidimensional SCG records cardiac vibrations in linear and rotational dimensions, and scalar parameters of kinetic energy can be computed. We speculate that AMI and revascularization profoundly modify cardiac kinetic energy as recorded by SCG.

Methods: Under general anesthesia, 21 swine underwent 90 min of myocardial ischemia induced by percutaneous sub-occlusion of the proximal left anterior descending (LAD) coronary artery and subsequent revascularization. Invasive hemodynamic parameters were continuously recorded. SCG was recorded during baseline, immediately and 80 min after LAD sub-occlusion, and immediately and 60 min after LAD reperfusion. iK was automatically computed for each cardiac cycle (iK^CC) in linear (iK_Lin) and rotational (iK_Rot) dimensions. iK was calculated as well during systole and diastole (iK^Sys and iK^Dia, respectively). Echocardiography was performed at baseline and after revascularization, and the left ventricle ejection fraction (LVEF) along with regional left ventricle (LV) wall abnormalities were evaluated.

Results: Upon LAD sub-occlusion, 77% of STEMI and 24% of NSTEMI were observed. Compared to baseline, troponins increased from 13.0 (6.5; 21.3) ng/dl to 170.5 (102.5; 475.0) ng/dl, and LVEF dropped from 65.0 ± 0.0 to 30.6 ± 5.7% at the end of revascularization (both p < 0.0001). Regional LV wall abnormalities were observed as follows: anterior MI, 17.6% (three out of 17); septal MI, 5.8% (one out of 17); antero-septal MI, 47.1% (eight out of 17); and infero-septal MI, 29.4% (five out of 17). In the linear dimension, iKLinCC, iKLinSys, and iKLinDia dropped by 43, 52, and 53%, respectively (p < 0.0001, p < 0.0001, and p = 0.03, respectively) from baseline to the end of reperfusion. In the rotational dimension, iKRotCC and iKRotSys dropped by 30 and 36%, respectively (p = 0.0006 and p < 0.0001, respectively), but iKRotDia did not change (p = 0.41). All the hemodynamic parameters, except the pulmonary artery pulse pressure, were significantly correlated with the parameters of iK, except for the diastolic component.

Conclusions: In this very context of experimental AMI with acute LV regional dysfunction and no concomitant AMI-related heart valve disease, linear and rotational iK parameters, in particular, systolic ones, provide reliable information on LV contractile dysfunction and its effects on the downstream circulation. Multidimensional SCG may provide information on the cardiac contractile status expressed in terms of iK during AMI and reperfusion. This automatic system may empower health care providers and patients to remotely monitor cardiovascular status in the near future.

Kinocardiography Derived from Ballistocardiography and Seismocardiography Shows High Repeatability in Healthy Subjects

Recent years have witnessed an upsurge in the usage of ballistocardiography (BCG) and seismocardiography (SCG) to record myocardial function both in normal and pathological populations. Kinocardiography (KCG) combines these techniques by measuring 12 degrees-of-freedom of body motion produced by myocardial contraction and blood flow through the cardiac chambers and major vessels. The integral of kinetic energy (iK) obtained from the linear and rotational SCG/BCG signals, and automatically computed over the cardiac cycle, is used as a marker of cardiac mechanical function. The present work systematically evaluated the test-retest (TRT) reliability of KCG iK derived from BCG/SCG signals in the short term (<15 min) and long term (3-6 h) on 60 healthy volunteers. Additionally, we investigated the difference of repeatability with different body positions. First, we found high short-term TRT reliability for KCG metrics derived from SCG and BCG recordings. Exceptions to this finding were limited to metrics computed in left lateral decubitus position where the TRT reliability was moderate-to-high. Second, we found low-to-moderate long-term TRT reliability for KCG metrics as expected and confirmed by blood pressure measurements. In summary, KCG parameters derived from BCG/SCG signals show high repeatability and should be further investigated to confirm their use for cardiac condition longitudinal monitoring.

Wearable Devices for Ambulatory Cardiac Monitoring: JACC State-of-the-Art Review

Ambulatory monitoring devices are enabling a new paradigm of health care by collecting and analyzing long-term data for reliable diagnostics. These devices are becoming increasingly popular for continuous monitoring of cardiac diseases. Recent advancements have enabled solutions that are both affordable and reliable, allowing monitoring of vulnerable populations from the comfort of their homes. They provide early detection of important physiological events, leading to timely alerts for seeking medical attention. In this review, the authors aim to summarize the recent developments in the area of ambulatory and remote monitoring solutions for cardiac diagnostics. The authors cover solutions based on wearable devices, smartphones, and other ambulatory sensors. The authors also present an overview of the limitations of current technologies, their effectiveness, and their adoption in the general population, and discuss some of the recently proposed methods to overcome these challenges. Lastly, we discuss the possibilities opened by this new paradigm, for the future of health care and personalized medicine.

Noninvasive Assessment of Cardiac Output in Advanced Heart Failure and Heart Transplant Candidates Using the Bioreactance Method

OBJECTIVES

The aim of the present study was to assess the validity and trending ability of the bioreactance method in estimating cardiac output at rest and in response to stress in advanced heart failure patients and heart transplant candidates.

DESIGN

This was a prospective single-center study.

SETTING

This study was conducted at the heart transplant center at the Freeman Hospital, Newcastle upon Tyne, UK.

PARTICIPANTS

Eighteen patients with advanced chronic heart failure due to reduced left ventricular ejection fraction (19 ± 7%), and peak oxygen consumption 12.3 ± 3.9 mL/kg/min.

INTERVENTIONS

Participants underwent right heart catheterization using the Swan-Ganz catheter.

MEASUREMENTS AND MAIN RESULTS

Cardiac output was measured simultaneously using thermodilution and bioreactance at rest and during active straight leg raise test to volitional exertion. There was no significant difference in cardiac index values obtained by the thermodilution and bioreactance methods (2.26 ± 0.59 v 2.38 ± 0.50 L/min, p > 0.05) at rest and peak straight leg raise test (2.92 ± 0.77 v 3.01 ± 0.66 L/min, p > 0.05). In response to active leg raise test, thermodilution cardiac output increased by 22% and bioreactance by 21%. There was also a strong relationship between cardiac outputs from both methods at rest (r = 0.88, p < 0.01) and peak straight leg raise test (r = 0.92, p < 0.01). Cartesian plot analysis showed good trending ability of bioreactance compared with thermodilution (concordance rate = 93%) CONCLUSIONS: `Cardiac output measured by the bioreactance method is comparable to that from the thermodilution method. Bioreactance method may be used in clinical practice to assess hemodynamics and improve management of advanced heart failure patients undergoing heart transplant assessment.

Inpatient cardiac monitoring using a patch-based mobile cardiac telemetry system during the COVID-19 pandemic

INTRODUCTION

Coronavirus disease 2019 (COVID-19) is a worldwide pandemic, and cardiovascular complications and arrhythmias in these patients are common. Cardiac monitoring is recommended for at risk patients; however, the availability of telemetry capable hospital beds is limited. We sought to evaluate a patch-based mobile telemetry system for inpatient cardiac monitoring during the pandemic.

METHODS

A prospective cohort study was performed of inpatients hospitalized during the pandemic who had mobile telemetry devices placed; patients were studied up until the time of discharge or death. The primary outcome was a composite of management changes based on data obtained from the system and detection of new arrhythmias. Other clinical outcomes and performance characteristics of the mobile telemetry system were studied.

RESULTS

Eighty-two patients underwent mobile telemetry device placement, of which 31 (37.8%) met the primary outcome, which consisted of 24 (29.3%) with new arrhythmias detected and 18 (22.2%) with management changes. Twenty-one patients (25.6%) died during the study, but none from primary arrhythmias. In analyses, age and heart failure were associated with the primary outcome. Monitoring occurred for an average of 5.3 ± 3.4 days, with 432 total patient-days of monitoring performed; of these, QT-interval measurements were feasible in 400 (92.6%).

CONCLUSION

A mobile telemetry system was successfully implemented for inpatient use during the COVID-19 pandemic and was shown to be useful to inform patient management, detect occult arrhythmias, and monitor the QT-interval. Patients with advanced age and structural heart disease may be more likely to benefit from this system.

Forcecardiography: A Novel Technique to Measure Heart Mechanical Vibrations onto the Chest Wall

This paper presents forcecardiography (FCG), a novel technique to measure local, cardiac-induced vibrations onto the chest wall. Since the 19th century, several techniques have been proposed to detect the mechanical vibrations caused by cardiovascular activity, the great part of which was abandoned due to the cumbersome instrumentation involved. The recent availability of unobtrusive sensors rejuvenated the research field with the most currently established technique being seismocardiography (SCG). SCG is performed by placing accelerometers onto the subject's chest and provides information on major events of the cardiac cycle. The proposed FCG measures the cardiac-induced vibrations via force sensors placed onto the subject's chest and provides signals with a richer informational content as compared to SCG. The two techniques were compared by analysing simultaneous recordings acquired by means of a force sensor, an accelerometer and an electrocardiograph (ECG). The force sensor and the accelerometer were rigidly fixed to each other and fastened onto the xiphoid process with a belt. The high-frequency (HF) components of FCG and SCG were highly comparable (r > 0.95) although lagged. The lag was estimated by cross-correlation and resulted in about tens of milliseconds. An additional, large, low-frequency (LF) component, associated with ventricular volume variations, was observed in FCG, while not being visible in SCG. The encouraging results of this feasibility study suggest that FCG is not only able to acquire similar information as SCG, but it also provides additional information on ventricular contraction. Further analyses are foreseen to confirm the advantages of FCG as a technique to improve the scope and significance of pervasive cardiac monitoring.

Modeling Consistent Dynamics of Cardiogenic Vibrations in Low-Dimensional Subspace

The seismocardiogram (SCG) measures the movement of the chest wall in response to underlying cardiovascular events. Though this signal contains clinically-relevant information, its morphology is both patient-specific and highly transient. In light of recent work suggesting the existence of population-level patterns in SCG signals, the objective of this study is to develop a method which harnesses these patterns to enable robust signal processing despite morphological variability. Specifically, we introduce seismocardiogram generative factor encoding (SGFE), which models the SCG waveform as a stochastic sample from a low-dimensional subspace defined by a unified set of generative factors. We then demonstrate that during dynamic processes such as exercise-recovery, learned factors correlate strongly with known generative factors including aortic opening (AO) and closing (AC), following consistent trajectories in subspace despite morphological differences. Furthermore, we found that changes in sensor location affect the perceived underlying dynamic process in predictable ways, thereby enabling algorithmic compensation for sensor misplacement during generative factor inference. Mapping these trajectories to AO and AC yielded R2 values from 0.81-0.90 for AO and 0.72-0.83 for AC respectively across five sensor positions. Identification of consistent behavior of SCG signals in low dimensions corroborates the existence of population-level patterns in these signals; SGFE may also serve as a harbinger for processing methods that are abstracted from the time domain, which may ultimately improve the feasibility of SCG utilization in ambulatory and outpatient settings.

An Analysis of the Effects of Noisy Electrocardiogram Signal on Heartbeat Detection Performance

Heartbeat detection for ambulatory cardiac monitoring is more challenging as the level of noise and artefacts induced by daily-life activities are considerably higher than monitoring in a hospital setting. It is valuable to understand the relationship between the characteristics of electrocardiogram (ECG) noises and the beat detection performance in the cardiac monitoring system. For this purpose, three well-known algorithms for the beat detection process were re-implemented. The beat detection algorithms were validated using two types of ambulatory datasets, which were the ECG signal from the MIT-BIH Arrhythmia Database and the simulated noise-contaminated ECG signal with different intensities of baseline wander (BW), muscle artefact (MA) and electrode motion (EM) artefact from the MIT-BIH Noise Stress Test Database. The findings showed that signals contaminated with noise and artefacts decreased the potential of beat detection in ambulatory signal with the poorest performance noted for ECG signal affected by the EM artefacts. In conclusion, none of the algorithms was able to detect all QRS complexes without any false detection at the highest level of noise. The EM noise influenced the beat detection performance the most in comparison to the MA and BW noises that resulted in the highest number of misdetections and false detections.

Cardiotoxicity Surveillance and Risk of Heart Failure During HER2 Targeted Therapy

BACKGROUND

Guidelines recommend left ventricular ejection fraction (LVEF) assessments every 3 months for cardiotoxicity monitoring during human epidermal growth factor receptor 2 (HER2) targeted therapy. Evidence in support of this practice is lacking.

OBJECTIVES

This study examines the association between adherence to cardiotoxicity surveillance guidelines and heart failure (HF) in HER2-positive breast cancer patients.

METHODS

A case-control study was performed in 53 patients who developed cardiotoxicity during HER2 targeted therapy, and 159 controls matched by age, anthracycline exposure, and year of treatment. Cardiotoxicity was defined as HF (New York Heart Association functional class III or IV) or cardiac death. Adherence to cardiotoxicity surveillance guidelines was ascertained from the beginning of HER2 targeted therapy to the diagnosis date of HF for cases or the corresponding timepoint for matched controls. Conditional logistic regression was used for case-control comparisons.

RESULTS

Eighty-one percent of cases and controls were previously treated with an anthracycline. Adherence to cardiotoxicity surveillance guidelines during the entire observation period or during the first 6 months of treatment was not associated with lower risk of HF. An LVEF <55% at any surveillance timepoint was identified in 49% of cases and 3% of controls, and an LVEF <55% during the final surveillance timepoint before developing HF was identified in 54% of cases and 4% of controls. In multivariable-adjusted analyses, LVEF <55% at any timepoint or during the final surveillance timepoint (odds ratio: 27.0; 95% confidence interval: 9.3 to 78.8 and odds ratio: 25.6; 95% confidence interval: 7.3 to 90.3, respectively) was associated with HF.

CONCLUSIONS

Patients with LVEF <55% on routine surveillance during HER2 targeted therapy are at increased risk for HF. Additional studies to define their optimal management are warranted.

A Unified Framework for Quality Indexing and Classification of Seismocardiogram Signals

The seismocardiogram (SCG) is a noninvasively-obtained cardiovascular bio-signal that has gained traction in recent years, however is limited by its susceptibility to noise and motion artifacts. Because of this, signal quality must be assured before data are used to inform clinical care. Common methods of signal quality assurance include signal classification or assignment of a numerical quality index. Such tasks are difficult with SCG because there is no accepted standard for signal morphology. In this paper, we propose a unified method of quality indexing and classification that uses multi-subject-based methods to overcome this challenge. Dynamic-time feature matching is introduced as a novel method of obtaining the distance between a signal and reference template, with this metric, the signal quality index (SQI) is defined as a function of the inverse distance between the SCG and a large set of template signals. We demonstrate that this method is able to stratify SCG signals on held-out subjects based on their level of motion-artifact corruption. This method is extended, using the SQI as a feature for classification by ensembled quadratic discriminant analysis. Classification is validated by demonstrating, for the first time, both detection and localization of SCG sensor misplacement, achieving an F1 score of 0.83 on held-out subjects. This paper may provide a necessary step toward automating the analysis of SCG signals, addressing many of the key limitations and concerns precluding the method from being widely used in clinical and physiological sensing applications.

Fast-Track Long Term Continuous Heart Monitoring in a Stroke Clinic: A Feasibility Study

Background: Paroxysmal atrial fibrillation (PAF) or flutter is prevalent among patients with cryptogenic stroke. The goal of this study was to investigate the feasibility of incorporating a fast-track, long term continuous heart monitoring (LTCM) program within a stroke clinic. Method: We designed and implemented a fast-track LTCM program in our stroke clinics. The instrument that we used for the study was the ZioXT® device from IRhythm™ Technologies. To implement the program, all clinic support staff received training on the skin preparation and proper placement of the device. We prospectively followed every patient who had a request from one of our inpatient or outpatient stroke or neurology providers to receive LTCM. We recorded patients' demographics, the LTCM indication, as well as related quality measures including same-visit placement, wearing time, analyzable time, LTCM application to the preliminary finding time, as well as patients' out of pocket cost. Results: Out of 501 patients included in the study, 467 (93.2%) patients (mean age 65.9 ± 13; men: 48%) received LTCM; and 92.5% of the patients had the diagnosis of stroke or TIA. 93.7% of patients received their LTCM during the same outpatient visit in the stroke clinic. The mean wearing time for LTCM was 12.1 days (out of 14 days). The average analyzable time among our patients was 95.0%. Eighteen (3.9%, 95%CI: 2.4-6.0) patients had at least one episode of PAF that was sustained for more than 30 s. The rate of PAF was 5.9% (95% CI: 3.5-9.2) among patients with the diagnosis of stroke. Out of 467 patients, 392 (84%) had an out-of-pocket cost of < $100. Conclusion: It is feasible to implement a fast-track cardiac monitoring as part of a stroke clinic with proper training of stroke providers, clinic staff, and support from a cardiology team.

Cardiotoxicity and Cardiac Monitoring Among Chemotherapy-Treated Breast Cancer Patients

OBJECTIVES

This study sought to determine the rate of chemotherapy-related cardiotoxicity and to estimate adherence to recommendations for cardiac monitoring among breast cancer patients treated with chemotherapy.

BACKGROUND

Heart failure (HF) is a known complication associated with cancer therapies. Little is known regarding the rate of chemotherapy-related cardiotoxicity and adherence to recommendations for cardiac monitoring among chemotherapy-treated breast cancer patients.

METHODS

Patients >18 years of age with a diagnosis of nonmetastatic invasive breast cancer between 2009 and 2014, treated with chemotherapy within 6 months of their diagnosis, were identified in the Truven Health MarketScan (IBM Watson Health, Cambridge, Massachusetts) database. HF, comorbidities, and treatment details were identified using diagnosis and billing codes. Analyses included descriptive statistics, Cox proportional hazard regression, and logistic regression.

RESULTS

A total of 16,456 patients were included; the median age was 56 years old. Cardiotoxicity was identified in 4.2% of patients. Therapy with trastuzumab (hazard ratio [HR]: 2.01; 95% confidence interval [CI]: 1.72 to 2.36) and anthracyclines (HR: 1.53; 95% CI: 1.30 to 1.80), Deyo comorbidity scores (HR: 1.38; 95% CI: 1.15 to 1.66; HR: 2.47; 95% CI: 1.94 to 3.15 for scores of 1 and ≥2, respectively), hypertension (HR: 1.28, 95% CI: 1.09 to 1.51), and valve disease (HR: 1.93; 95% CI: 1.48 to 2.51) were associated with an increased risk of cardiotoxicity. Patients ≤35 years of age (HR: 0.37; 95% CI: 0.19 to 0.72) and 36 to 49 years of age (HR: 0.49; 95% CI: 0.38 to 0.62) were less likely to have cardiotoxicity than patients 65 years of age and older. Among 4,325 patients treated with trastuzumab, guideline-adherent cardiac monitoring was identified in 46.2% of patients. Therapies using anthracyclines (odds ratio [OR]: 1.58; 95% CI: 1.35 to 1.87), taxanes (OR: 1.63; 95% CI: 1.27 to 2.08), and radiation (OR: 1.22; 95% CI: 1.08 to 1.39) were associated with guideline-adherent monitoring.

CONCLUSIONS

HF is an uncommon complication of breast cancer therapies. The risk was higher among patients treated with trastuzumab or anthracyclines and lower in younger patients. Cardiac monitoring among trastuzumab-treated patients should be a priority among high-risk patients and in the presence of comorbidities or other chemotherapies such as those using anthracyclines.

Cardiac arrhythmia detection outcomes among patients monitored with the Zio patch system: a systematic literature review

Objective: Cardiac arrhythmias can be serious and life threatening, and can impose a significant burden on healthcare systems. Recent technological advances in ambulatory electrocardiogram recorders have led to the development of unobtrusive wearable biosensors which allow physicians to study patients' continuous cardiac rhythm data collected over multiple weeks. The objective of this systematic literature review was to summarize evidence on the clinical effectiveness of the Zio ¹ patch, a long-term, continuous, uninterrupted cardiac monitoring system. Methods: Findings from searches of MEDLINE, Embase and the Cochrane Central Register of Controlled Trials, as well as grey literature, were screened by two reviewers to identify studies reporting cardiac arrhythmia detection outcomes among patients monitored with Zio for an intended duration ≥7 days. Results: Twenty-three publications (22 unique studies) were identified. The unweighted mean wear time was 10.4 days (median ranging from 5 to 14 days). The rate of arrhythmia detection increased with monitoring durations >48 h and continued to increase beyond 7 days of monitoring. Across the 22 studies, unweighted mean detection rates for atrial fibrillation (AF; n = 15), supraventricular tachycardia or supraventricular ectopy (n = 15), and ventricular tachycardia (n = 15) were 12.2%, 45.5% and 17.3%, respectively. Unweighted mean detection rates for chronic/sustained AF (n = 5) and paroxysmal AF (n = 5) were 5.6% and 23.3%, respectively. Conclusion: Findings from the review suggest that long-term, continuous, uninterrupted monitoring with Zio results in longer patient wear times and higher cardiac arrhythmia detection rates compared with outcomes reported in previous reviews of short-duration (24-48 h) cardiac rhythm recording studies.

Cardio-Respiratory Monitoring in Archery Using a Smart Textile Based on Flexible Fiber Bragg Grating Sensors

In precision sports, the control of breathing and heart rate is crucial to help the body to remain stable in the shooting position. To improve stability, archers try to adopt similar breathing patterns and to have a low heartbeat during each shot. We proposed an easy-to-use and unobtrusive smart textile (ST) which is able to detect chest wall excursions due to breathing and heart beating. The sensing part is based on two FBGs housed into a soft polymer matrix to optimize the adherence to the chest wall and the system robustness. The ST was assessed on volunteers to figure out its performance in the estimation of respiratory frequency (f_R) and heart rate (HR). Then, the system was tested on two archers during four shooting sessions. This is the first study to monitor cardio-respiratory activity on archers during shooting. The good performance of the ST is supported by the low mean absolute percentage error for f_R and HR estimation (≤1.97% and ≤5.74%, respectively), calculated with respect to reference signals (flow sensor for f_R, photopletismography sensor for HR). Moreover, results showed the capability of the ST to estimate f_R and HR during different phases of shooting action. The promising results motivate future investigations to speculate about the influence of f_R and HR on archers' performance.

Diagnostic accuracy of a single-lead portable ECG device for measuring QTc prolongation

Background

To assess the diagnostic accuracy of a single‐lead portable ECG device for measuring QTc‐intervals in comparison with a standard 12‐lead ECG.

Methods

Adult patients visiting the cardiology outpatient clinic for a 12‐lead recording were also measured with a portable single‐lead ECG recorder (HeartcheckTM). QTc‐intervals were determined by two cardiologists. Perfect agreement was defined as a limit of ≤10 ms between the two measurement methods.

Results

Hundred one ECGs were recorded. QTc‐interval mean differences between the two measurement methods was substantially outside our definition of perfect agreement (‐31.9 [SD±41.3] ms).

Conclusion

In conclusion, the Heartcheck single‐lead ECG device is not accurate for measuring QTc‐intervals.

Interventions to Decrease Overuse of Cardiac Monitoring (Telemetry) When Transitioning from the Intensive Care Unit to the Regular Nursing Floor

Background

Cardiac monitoring (telemetry) is a common over-utilized hospital resource in the United States. Previous studies have shown that telemetry does not improve outcomes for low-risk patients. Inappropriate utilization occurs because of lack of awareness of guideline-based indications or non-adherence to known indications.

Objective

A quality improvement study was conducted to reduce telemetry overutilization during the transition of care from the intensive care unit (ICU) by 15% through increasing awareness of indications for telemetry.

Methods

The study cohort included patients originally admitted to the ICU for sepsis who had improved and were stable for transfer to a non-ICU setting. Subjects were identified and included during pre-intervention (six weeks) and intervention (six weeks) periods. Resident physicians and nurse practitioners were targeted using multiple modalities of education: didactic lectures during week one, poster demonstrations during week three, and video presentations during week five.

Results

A total of 246 study subjects during the pre-intervention and 94 study subjects in the intervention period were studied; 187 of the 246 subjects in the pre-intervention arm (76%) and 58 of the 94 subjects in the intervention arm (61.7%) were transferred with telemetry. Telemetry utilization dropped by 23.1% at the end of the intervention period.

Conclusion

Educating the caregivers about the indications for telemetry led to a decrease in over-utilization of telemetry on the transition of care from the ICU to the regular nursing floor. Repetitive and multi-modality educational interventions were effective tools and associated with increased adherence to established guidelines for telemetry usage.

Subclinical atrial fibrillation in patients with recent transient ischemic attack

BACKGROUND

Atrial fibrillation (AF) is a major risk factor of stroke, but the association between AF and transient ischemic attack (TIA) is less clear. Despite this, patients with TIA are included in stroke trials.

AIMS

To determine the 1-year incidence of AF in TIA patients using an insertable cardiac monitor (ICM); second, to determine factors associated with incident AF in these patients.

METHODS

Prospective cohort study of patients with TIA with normal standard electrocardiogram (ECG) and 72-hour Holter monitoring (HM). Exclusion criteria were as follows: age < 18 or > 81 years; prior AF/stroke; ongoing oral anticoagulation therapy or contraindication for it; significant carotid artery stenosis; uncertain TIA diagnosis. Eligible patients received an ICM and were followed for 12 months.

RESULTS

From November 2013 to October 2015, 809 patients were diagnosed with TIA. In total, 235 patients were eligible. Nine (3.8%) of these had AF on standard ECG or HM. Of the remaining patients, 121 refused ICM implantation. In total, 105 patients (median age 65.4 years [range 27.1-80.8], 46% males) received an ICM, which revealed AF in 7 (6.7%). Factors associated with new-onset AF were a history of recurrent TIA (odds ratio [OR] 11.5, 95% confidence interval [CI] 2.1-63.6) and heart failure (OR 12.7, 95% CI 1.71-96.83).

CONCLUSIONS

The 1-year incidence of AF in TIA patients with normal ECG and HM was 6.7% using an ICM. Factors associated with development of AF were recurrent TIA and heart failure.

Responsiveness of Noninvasive Continuous Cardiac Output Monitoring During the Valsalva Maneuver

To describe the baseline hemodynamic variables and response time of hemodynamic changes associated with the Valsalva maneuver using noninvasive continuous cardiac output monitoring (Nexfin). Hemodynamic monitoring provides an integral component of advanced clinical care and the ability to monitor response to treatment interventions. The emergence of noninvasive hemodynamic monitoring provides clinicians with an opportunity to monitor and assess patients rapidly with ease of implementation. However, the responsiveness of this method in tracking dynamic changes that occur has not been fully elucidated. A prospective observational study was conducted involving 44 healthy volunteers (age = 38 ±12 years). Participants performed a Valsalva maneuvers to illicit dynamic changes in blood pressure, cardiac output, cardiac index, systemic vascular resistance index (SVRI), and stroke volume. Changes in these hemodynamic parameters were monitored while performing repeated standardized Valsalva maneuvers. Baseline hemodynamic values were obtained in all 44 participants, and showed an interaction with age, accompanying a significant decline in cardiac index (r = -.66, p < .05) and stroke volume (r = -.68,p < .05), and an increase in SVRI (r = .67, p < .05) with increasing age. The Valsalva maneuver, performed in 20 participants, resulted in a change of 10% from baseline blood pressure and cardiac index, which was detected within 4.53 s (SD = 4.36) and 3.31 s (SD = 2.21), respectively. Noninvasive continuous cardiac monitoring demonstrated the ability to rapidly detect logical and predictable hemodynamic changes. These observations suggest that such Nexfin technology may have useful clinical applications.

The Interplay between Obstructive Sleep Apnea and Atrial Fibrillation

Atrial fibrillation (AF) is the most common cardiac arrhythmia. Obstructive sleep apnea (OSA) is also an increasingly common condition. Both entities are risk factors for ischemic stroke and both conditions are linked with increased mortality. Mechanical effects of obesity and sleep apnea can lead to increased afterload, left ventricular hypertrophy, and left atrial fibrosis and remodeling. These changes can result in an increased risk of AF development. The current paper summarizes the evidence for the bidirectional relationship between AF and OSA. The merits of selective screening for these two conditions are also discussed.

Does this patient need telemetry? An analysis of telemetry ordering practices at an academic medical center

INTRODUCTION

The American Heart Association and Choosing Wisely campaign recommend guideline-based usage of telemetry. Inappropriate use leads to increased costs, alarm fatigue, and inefficient nursing care. This study assesses provider ordering practices for telemetry at a US-based academic hospital.

METHODS

This retrospective study includes all telemetry orders in the medicine and progressive care units from April 2014 to March 2015. Indications were grouped into categories per American Heart Association guidelines.

RESULTS

The top 3 cardiac indications included angina/acute coronary syndrome (35.3%), arrhythmias (19.7%), and heart failure (10.2%). However, noncardiac indications accounted for 20.2% of orders, including respiratory conditions (17.4%), infection (17.4%), substance abuse (14.0%), bleeding (12.4%), vital sign monitoring (10.4%), altered mental status (7.0%), and pulmonary embolus/deep vein thrombosis (7.0%).

CONCLUSIONS

One-fifth of patients were monitored on telemetry for noncardiac indications. We recommend further study on the benefits and risks of telemetry in these patients and systems-based changes for appropriate usage.

Monitoring for AF: Identifying the Burden of Atrial Fibrillation and Assessing Post-Ablation

The management of atrial fibrillation (AF) is among the most challenging aspects of cardiology and uncertainties abound concerning stroke assessment and stroke risk reduction. Currently, AF is viewed as a dichotomous variable (fully present or absent) when it comes to stroke risk; there is no regard to the amount of AF either spontaneously or due to rhythm control strategies. For this reason, monitoring in patients with a known AF history, particularly after ablation, has focused on easily measured outcomes such as time to recurrence. However, emerging data suggest that thresholds exist between stroke risk and AF quantity as measured by either duration or burden. As a result, there is an increasing interest in long-term continuous monitoring following a rhythm control strategy to assess efficacy beyond typical symptom reduction. Insertable cardiac monitors (ICMs) with AF-sensing algorithms and remote data transmission capabilities can be used for this purpose, and wearable devices with similar functions are on the horizon. In addition to their diagnostic potential, these tools are also being used therapeutically with efforts to target anticoagulation therapy only in response to AF episodes.

Role of hospitalists in the diagnosis of atrial fibrillation for the management of cryptogenic stroke patients

Cryptogenic strokes are responsible for significant morbidity and mortality. Identifying the underlying cause of cryptogenic stroke is imperative for appropriate short and long-term management of these patients. In particular, detecting atrial fibrillation in cryptogenic stroke patients may shed insight into the cause of the index stroke, but is also important to identify an important cause of secondary stroke. There is accumulating evidence indicating that monitoring for durations beyond the guideline recommended 30 day-period results in greater atrial fibrillation yield. This article reviews current guidelines and practices for the diagnosis of cryptogenic stroke, as well as outpatient cardiac monitoring options available, and focuses on the role that hospitalists have to play in the care of these patients.

Detection of Atrial Fibrillation Among Patients With Stroke Due to Large or Small Vessel Disease: A Meta-Analysis

Background

Recent trials have demonstrated that extended cardiac monitoring increases the yield of paroxysmal atrial fibrillation (AF) detection in patients with cryptogenic stroke. The utility of extended cardiac monitoring is uncertain among patients with stroke caused by small and large vessel disease. We conducted a meta‐analysis to estimate the yield of AF detection in this population.

Methods and Results

We searched PubMed, Cochrane, and SCOPUS databases for studies on AF detection in stroke patients and excluded studies restricted to patients with cryptogenic stroke or transient ischemic attack. We abstracted AF detection rates for 3 populations grouped by stroke etiology: large vessel stroke, small vessel stroke, and stroke of undefined etiology (a mixture of cryptogenic, small vessel, large vessel, and other stroke etiologies). Our search yielded 30 studies (n=5687). AF detection rates were similar in patients with large vessel (2.2%, 95% CI 0.3–5.5; n=830) and small vessel stroke (2.4%, 95% CI 0.4–6.1; n=520). No studies had a monitoring duration longer than 7 days. The yield of AF detection in the undefined stroke population was higher (9.2%; 95% CI 7.1–11.5) compared to small vessel stroke (P=0.02) and large vessel stroke (P=0.02) populations.

Conclusions

AF detection rate is similar in patients with small and large vessel strokes (2.2–2.4%). Because no studies reported on extended monitoring (>7 days) in these stroke populations, we could not estimate the yield of AF detection with long‐term cardiac monitoring. Randomized controlled trials are needed to examine the utility of AF detection with long‐term cardiac monitoring (>7 days) in this patient population.