A case report of fludarabine associated ectopic atrial bradycardia and literature review of fludarabine induced bradycardia.

BACKGROUND: Fludarabine is a chemotherapeutic agent with lymphodepleting effects that is increasingly used as part of a conditioning regimen prior to allogeneic stem cell transplantation. Fludarabine is generally considered a relatively safe medication with only rare cases of cardiotoxic side effects. CASE PRESENTATION: Here, we present a case of a 30-year-old woman who was undergoing conditioning for a haploidentical cell transplantation for treatment of Fanconi anemia with a 5-day course of daily fludarabine infusion. After her second fludarabine infusion, she was noted to have ectopic atrial bradycardia that resolved with supportive therapy and completion of fludarabine infusion. CONCLUSION: We report the first case of ectopic atrial bradycardia associated with fludarabine. Although rare and transient, clinicians should recognize this rare cardiotoxic side effect of fludarabine.

Predictors of paroxysmal atrial fibrillation in patients with a cryptogenic stroke: Selecting patients for long-term rhythm monitoring.

BACKGROUND: After a cryptogenic stroke, patients often will require prolonged cardiac monitoring; however, the subset of patients who would benefit from long-term rhythm monitoring is not clearly defined. OBJECTIVE: The purpose of this study was to create a risk score by identifying significant predictors of atrial fibrillation (AF) using age, sex, comorbidities, baseline 12-lead electrocardiogram, short-term rhythm monitoring, and echocardiographic data and to compare it to previously published risk scores. METHODS: Patients admitted to Montefiore Medical Center between May 2017 and June 2022 with a primary diagnosis of cryptogenic stroke or transient ischemic attack who underwent long-term rhythm monitoring with an implantable cardiac monitor were retrospectively analyzed. RESULTS: Variables positively associated with a diagnosis of clinically significant AF include age (P <.001), race (P = .022), diabetes status (P = .026), chronic obstructive pulmonary disease status (P = .012), presence of atrial runs (P = .003), number of atrial runs per 24 hours (P <.001), total number of atrial run beats per 24 hours (P <.001), number of beats in the longest atrial run (P <.001), left atrial enlargement (P = .007), and at least mild mitral regurgitation (P = .009). We created a risk stratification score for our population, termed the ACL score. The ACL score demonstrated superiority to the CHA2DS2-VASc score and comparability to the C2HEST score for predicting device-detected AF. CONCLUSION: The ACL score enables clinicians to better predict which patients are more likely to be diagnosed with device-detected AF after a cryptogenic stroke.

Premature ventricular complexes: Assessing burden density in a large national cohort to better define optimal ECG monitoring duration.

BACKGROUND: Premature ventricular contraction (PVC) burden is a risk factor for heart failure and cardiovascular death in patients with structural heart disease. Long-term electrocardiographic monitoring can have a significant impact on PVC burden evaluation by further defining PVC distribution patterns. OBJECTIVE: This study aimed to ascertain the optimal duration of electrocardiographic monitoring to characterize PVC burden and to understand clinical characteristics associated with frequent PVCs and nonsustained ventricular tachycardia in a large US cohort. METHODS: Commercial data (iRhythm's Zio patch) from June 2011 to April 2022 were analyzed. Inclusion criteria were age >18 years, PVC burden ≥5%, and wear period ≥13 days. PVC burden cutoffs were determined on the basis of AHA/ACC/HRS guidelines for very frequent PVCs (10,000-20,000 during 24 hours). Patients were assigned to categories by PVC densities: low, <10%; moderate, 10% to <20%; and high, ≥20%. Mean measured error was assessed at baseline and daily until the wear period's end for overall PVC burden and different PVC densities. RESULTS: Analysis of 106,705 patch monitors revealed a study population with mean age of 70.6 ± 14.6 years (33.6% female). PVC burden was higher in male patients and those >65 years of age. PVC burden mean error decreased from 2.9% at 24 hours to 1.3% at 7 days and 0.7% at 10 days. Number of ventricular tachycardia episodes per patient increased with increasing PVC burden (P < .0001). CONCLUSION: Extending ambulatory monitoring beyond 24 hours to 7 days or more improves accuracy of assessing PVC burden. Ventricular tachycardia frequency and duration vary by initial PVC density, highlighting the need for prolonged cardiac monitoring.

Utilization of the remote monitoring of cardiac implantable electronic devices in a diverse demographic cohort: Insights from a single-center observation.

BACKGROUND: Despite its clinical benefits, patient compliance to remote monitoring (RM) of cardiac implantable electronic devices (CIEDs) varies and remains under-studied in diverse populations. OBJECTIVE: We sought to evaluate RM compliance, clinical outcomes, and identify demographic and socioeconomic factors affecting RM in a diverse urban population in New York. METHODS: This retrospective cohort study included patients enrolled in CIED RM at Montefiore Medical Center between December 2017 and May 2022. RM compliance was defined as the percentage of days compliant to RM transmission divided by the total prescribed days of RM. Patients were censored when they were lost to follow-up or at the time of death. The cohorts were categorized into low (≤30%), intermediate (31-69%), and high (≥70%) RM compliance groups. Statistical analyses were conducted accordingly. RESULTS: Among 853 patients, median RM compliance was 55%. Age inversely affected compliance (p < .001), and high compliance was associated with guideline-directed medical therapy (GDMT) usage and implantable cardioverter defibrillator (ICD)/cardiac resynchronization defibrillator (CRTD) devices. The low-compliance group had a higher mortality rate and fewer regular clinic visits (p < .001) than high-compliance group. Socioeconomic factors did not significantly impact compliance, while Asians showed higher compliance compared with Whites (OR 3.67; 95% CI 1.08-12.43; p = .04). Technical issues were the main reason for non-compliance. CONCLUSION: We observed suboptimal compliance to RM, which occurred most frequently in older patients. Clinic visit compliance, optimal medical therapy, and lower mortality were associated with higher compliance, whereas insufficient understanding of RM usage was the chief barrier to compliance.

Low Utility of Short-Term Rhythm Assessment Before Long-Term Rhythm Monitoring in Patients With Cryptogenic Stroke.

Implantable cardiac monitors are routinely placed for long-term monitoring (LTM) after a period of negative short-term monitoring (STM) to increase atrial fibrillation (AF) detection after a cryptogenic stroke or transient ischemic attack (TIA). Optimizing AF monitoring after a cryptogenic stroke is critical to improve outcomes and reduce costs. We sought to compare the diagnostic yield of STM versus LTM, assess the impact of routine STM on hospitalization length of stay, and perform a financial analysis comparing the current model to a theoretical model wherein patients can proceed directly to LTM. Our retrospective observational cohort study analyzed patients admitted to Montefiore Medical Center between May 2017 and June 2022 with a primary diagnosis of cryptogenic stroke or TIA who underwent Holter device monitoring. Of 396 subjects, STM detected AF in 10 (2.5%) compared with a diagnostic yield of 14.6% for LTM (median time to diagnosis of 76 days). Of the 386 patients with negative STM, 130 (33.7%) received an implantable cardiac monitor while an inpatient, and 256 (66.3%) did not. We calculated a point estimate of 1.67 days delay of discharge attributable to the requirement for STM to precede LTM. Our model showed that the expected cost per patient in the STM-first paradigm is $28,615.33 versus $27,111.24 in the LTM-or-STM paradigm. Considering the relatively lower diagnostic yield of STM and its association with a longer length of stay and higher costs, it may be reasonable to proceed directly to LTM to optimize AF detection after a cryptogenic stroke or TIA.

Anatomical considerations and clinical interpretation of the 12-lead ECG in the prone position: a prospective multicentre study.

AIMS: The aim of this study is to provide guidance for the clinical interpretation of electrocardiograms (ECGs) in prone position and to establish the electroanatomic explanations for the possible differences to supine position ECGs that may be observed. Additionally, to determine if prone back ECG can be used as an alternative to standard ECG in patients who may benefit from prone position. METHODS AND RESULTS: The ECG in supine (standard ECG), prone back (precordial leads placed on the patient's back), and prone anterior position (precordial leads placed in the standard position with the subjects in prone position) were prospectively examined on 85 subjects. Comparisons of ECG parameters between these positions were performed. Computed tomography (CT) scans were performed in both positions to determine possible electroanatomic aetiologies for prone-associated ECG changes. There were significant differences in QRS amplitude in Leads V1-V5 between supine and prone positions. Q waves were more frequently observed in prone back position vs. supine position (V1: 74.1 vs. 10.6%, P < 0.0001; V2: 23.5 vs. 0%, P < 0.0001, respectively). Flat and inverted T waves were more common in prone back leads (V1: 98 vs. 66%, P < 0.0001; V2: 96 vs. 8%, P < 0.0001; V3: 45 vs. 7%, P < 0.0001). The 3D-CT reconstructions measurements corroborated the significant inverse correlation between QRS amplitude and the distance from the centre of the heart to the estimated lead positions. CONCLUSION: In prone back position ECG, low QRS amplitude should not be misinterpreted as low voltage conditions, neither should Q waves and abnormal T waves are considered anteroseptal myocardial infarction. These changes can be explained by an increased impedance (due to interposing lung tissue) and by the increased distance between the electrodes to the centre of the heart.

Monitoring conduction disturbances following TAVR: Feasibility study of early discharge using a novel telemetry patch.

INTRODUCTION: Despite the technological advances and increasing operator experience, the rate of permanent pacemaker implantation (PPI) after transcatheter aortic valve replacement (TAVR) has not decreased over time. With a continuous downward trend in post-TAVR length of stay, prolonged home-monitoring may have a key role in detecting potentially serious conduction abnormalities after TAVR discharge. METHODS: In this study, the ZioPatch-AT monitor was used to detect conduction abnormalities after TAVR discharge. The cardiac monitoring device was systematically provided to all patients having pre-existing right bundle branch block or developing intra-/peri-procedural conduction disturbances, in the absence of guideline indication for PPI at discharge. RESULTS: From a total of 75 patients at high-risk of conduction disturbances, 8 (11%) of them underwent PPI and most of them (6/8) were detected before symptoms' occurrence. Paired analysis between baseline and discharge electrocardiograms detected a significant widening of the QRS in all patients; on the contrary, PR length was significantly increased only in the group experiencing HAVB after discharge (p < 0.01). CONCLUSIONS: In an early post-TAVR discharge era, 30-day outpatient cardiac rhythm monitoring is potentially a safe solution to allow timely recognition of new conduction disturbances requiring PPI.

Arrhythmia patterns during and after hospitalization for COVID-19 infection detected via patch-based mobile cardiac telemetry.

BACKGROUND: Coronavirus infection is the cause of the current world-wide pandemic. Cardiovascular complications occur in 20-30% of patients with COVID-19 infection including myocardial injury and arrhythmias. Current understanding of specific arrhythmia type and frequency is limited. OBJECTIVE: We aimed to analyze arrhythmia type and frequency in patients with COVID infection, identifying arrhythmia patterns over time during hospitalization and post discharge utilizing a patch based mobile cardiac telemetry system. METHODS: A prospective cohort study during the COVID-19 pandemic was performed. We included in our study patients hospitalized with COVID-19 infection who had a patch-based mobile telemetry device placed for cardiac monitoring. RESULTS: Quantitative reports for 59 patients were available for analysis. Arrhythmias were detected in 72.9% of patients and at a consistent frequency throughout the monitoring period in 52.9%-89.5% of patients daily. The majority of arrhythmias were SVT (59.3% of patients) and AF (22.0%). New onset AF was noted in 15.0% of all patients and was significantly associated with older age (OR 1.4 for 5 yrs. difference; 95% CI 1.03-2.13). Of 9 patients who were discharged with continued patch monitoring, 7 (78%) had arrhythmic events during their outpatient monitoring period. CONCLUSION: In COVID-19 patients arrhythmias were observed throughout hospitalization with a consistent daily frequency. Patients continued to exhibit cardiac arrhythmias after hospital discharge of a type and frequency similar to that seen during hospitalization. These findings suggest that the risk of arrhythmia associated with COVID infection remains elevated throughout the hospital course as well as following hospital discharge.

Multiparameter diagnostic sensor measurements during clinically stable periods and worsening heart failure in ambulatory patients.

AIMS: This study aims to characterize the range of implantable device-based sensor values including heart sounds, markers of ventilation, thoracic impedance, activity, and heart rate for patients with heart failure (HF) when patients were deemed to be in clinically stable periods against the time course of acute decompensation and recovery from HF events. METHODS AND RESULTS: The MultiSENSE trial followed 900 patients implanted with a COGNIS CRT-D for up to 1 year. Chronic, ambulatory diagnostic sensor data were collected and evaluated during clinically stable periods (CSP: unchanged NYHA classification, no adverse events, and weight change ≤2.27 kg), and in the timeframe leading up to and following HF events (HF admissions or unscheduled visits with intravenous HF treatment). Physiologic sensor data from 1667 CSPs occurring in 676 patients were compared with those data leading up to and following 192 HF events in 106 patients. Overall, the mean age was 66.6 years, and the population were predominantly male (73%). Patients were primarily in NYHA II (67%), with a mean LVEF of 29.6% and median NT-proBNP of 754.5 pg/mL. Sensor values during CSP were poorer in patients who had HF events during the study period than those without HF events, including first heart sound (S1: 2.18 ± 0.84 mG vs. 2.62 ± 0.95 mG, P = 0.002), third heart sound (S3: 1.13 ± 0.36 mG vs. 0.91 ± 0.30 mG, P < 0.001), thoracic impedance (45.66 ± 8.78 Ohm vs. 50.33 ± 8.43 Ohm, P < 0.001), respiratory rate (19.09 ± 3.10 br/min vs. 17.66 ± 2.39 br/min, P = 0.002), night time heart rate (73.39 ± 8.36 b.p.m. vs. 69.56 ± 8.09 b.p.m., P = 0.001), patient activity (1.69 ± 1.84 h vs. 2.56 ± 2.20 h, P = 0.006), and HeartLogic index (11.07 ± 12.14 vs. 5.31 ± 5.13, P = 0.001). Sensor parameters measured worsening status leading up to HF events with recovery of values following treatment. CONCLUSIONS: Device-based physiologic sensors not only revealed progressive worsening leading up to HF events but also differentiated patients at increased risk of HF events when presumed to be clinically stable.

A Head-to Head Comparison of Machine Learning Algorithms for Identification of Implanted Cardiac Devices.

Application of artificial intelligence techniques in medicine has rapidly expanded in recent years. Two algorithms for identification of cardiac implantable electronic devices using chest radiography were recently developed: The PacemakerID algorithm, available as a mobile phone application (PIDa) and a web platform (PIDw) and The Pacemaker Identification with Neural Networks (PPMnn), available via web platform. In this study, we assessed the relative accuracy of these algorithms. The machine learning algorithms (PIDa, PIDw, PPMnn) were used to predict device manufacturer using chest X-rays for patients with implanted devices. Each prediction was considered correct if predicted certainty was >75%. For comparative purposes, accuracy of each prediction was compared to the result using the CARDIA-X algorithm. 500 X-rays were included from a convenience sample. Raw accuracy was PIDa 89%, PIDw 73%, PPMnn 71% and CARDIA-X 85%. In conclusion, machine learning algorithms for identification of cardiac devices are accurate at determining device manufacturer, have capacity for improved accuracy with additional training sets and can utilize simple user interfaces. These algorithms have clinical utility in limiting potential infectious exposures and facilitate rapid identification of devices as needed for device reprogramming.

Prevalence and association of the Wellens' sign with coronary artery disease in an ethnically diverse urban population.

BACKGROUND: Wellens' sign is considered to be an ominous sign indicative of underlying significant proximal left anterior descending artery stenosis. We sought to identify the prevalence of the Wellens' pattern in a large ethnically diverse urban population and assess its association with the presence and extent of coronary artery disease. METHODS: We utilized the MUSE ECG database of Montefiore Medical Center, an academic tertiary health care system, to identify ECGs from 2012 to 2019 exhibiting a Wellens' pattern. From a dataset of 1.76 million tracings, six screening diagnosis codes were selected to approximate the Wellens' pattern. These codes were used to generate a cohort of ECGs for manual review by a board certified cardiologist to determine if a Wellens' pattern was present. RESULTS: Of 1,756,742 ECGs performed on 433,218 patients from 2012 to 2019; after initial screening 2186 ECGs were identified for manual review. Of these, 448 (0.1%) patients were confirmed to have a Wellens' pattern. 229 patients underwent cardiac catheterization, while 219 patients were managed medically. No statistical difference was seen in the occurrence of Wellens' Type A and B pattern across the ethnic groups after multivariate analysis. Women were more likely to have Type B Wellens' compared to men (OR 2.40 (1.58, 3.62) P < 0.0001). 80 (35%) patients had single vessel LAD disease of which 22 (10%) had proximal, 40 (17%) had mid, 4 (1%) had distal stenosis, while diffuse LAD disease was seen in 14 (6%) patients. Two vessel disease was seen in 46 (20%) patients with a Wellens' pattern, and triple vessel disease was seen in 23 (10%) patients. Of note, 71 (31%) patients had either normal or nonobstructive coronary disease despite exhibiting a Wellens' pattern ECG. CONCLUSION: Wellens' sign is a rare electrocardiographic pattern which when seen in a patient with an appropriate clinical presentation, suggests but is not definitive for the presence of significant coronary disease, often but not exclusively in an LAD distribution. We found no statistical difference in the occurrence of Wellens' sign among different racial/ethnic groups. Patients with a Wellens' pattern may have critical lesions at a variety of LAD sites as well as in multiple vessels. As such, the interventionalist needs to be prepared for these uncertainties at the time of cardiac catheterization.

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.

Determining the optimal duration for premature ventricular contraction monitoring.

BACKGROUND: Premature ventricular contractions (VPC) have hour-to-hour and day-to-day variation. High VPC burden correlates with cardiomyopathy. OBJECTIVE: To determine the optimal duration for ambulatory electrocardiogram monitoring for accurate assessment of VPC burden. METHODS: Our group performed a retrospective analysis on patch monitors used for any indication with overall VPC burden ≥5.0% between February 1, 2016, and February 1, 2020. We generated cumulative daily VPC averages for each day of wear and performed linear regression analysis between each cumulative daily average and overall burden. Patients were divided into groups based on low or high VPC frequency, and the analysis was repeated. Split-sample validation was used to internally validate the overall prediction model. RESULTS: A total of 116 patches representing 107 patients (mean age: 64.5; female: 48%) were analyzed. Mean overall VPC burden was 13.4% ± 7.5% (range: 5.0%-42.0%). Day 1 R2 was 60%, P < .001, and continued to increase to R2 88%, P < .001 at day 14. Median percent and absolute error decreased from 22.70% (interquartile range [IQR]: 9.73-34.39) and 2.58% (IQR: 1.24-4.59) at day 1 to 5.62% (IQR: 2.82-8.39) and 0.55% (IQR: 0.28-1.05) at day 14. Patients with higher overall VPC frequencies achieved a more rapid rise in R2 relative to those with lower frequencies. Split-sample validation supported the internal validity of our linear regression prediction model. CONCLUSION: Mobile telemetry for a period of ∼7 days accurately reflects overall VPC burden. Measurement of VPC burden for only 24-48 hours may not accurately reflect total burden. Monitoring for 2 weeks or longer adds little additional VPC information.

QT prolongation in a diverse, urban population of COVID-19 patients treated with hydroxychloroquine, chloroquine, or azithromycin.

PURPOSE: Hydroxychloroquine, chloroquine, and azithromycin have been used for treatment of COVID-19, but may cause QT prolongation. Minority populations are disproportionately impacted by COVID-19. This study evaluates the risk of QT prolongation and subsequent outcomes after administration of these medications in largely underrepresented minority COVID-19 patients. METHODS: We conducted an observational study on hospitalized COVID-19 patients in the Montefiore Health System (Bronx, NY). We examined electrocardiograms (ECG) pre/post-medication initiation to evaluate QTc, HR, QRS duration, and presence of other arrhythmias. RESULTS: One hundred five patients (mean age 67 years; 44.8% F) were analyzed. The median time from the first dose of any treatment to post-medication ECG was 2 days (IQR: 1-3). QTc in men increased from baseline (440 vs 455 ms, p < 0.001), as well as in women (438 vs 463 ms, p < 0.001). The proportion of patients with QT prolongation increased significantly (14.3% vs 34.3%, p < 0.001) even when adjusted for electrolyte abnormalities. The number of patients whose QTc > 500 ms was significantly increased after treatment (16.2% vs. 4.8%, p < 0.01). Patients with either QTc > 500 ms or an increase of 60 ms had a higher frequency of death (47.6% vs. 22.6%, p = 0.02) with an odds ratio of 3.1 (95% CI: 1.1-8.7). Adjusting for race/ethnicity yielded no significant associations. CONCLUSIONS: Hydroxychloroquine, chloroquine, and/or azithromycin were associated with QTc prolongation but did not result in fatal arrhythmias. Our findings suggest that any harm is unlikely to outweigh potential benefits of treatment. Careful risk-benefit analyses for individual patients should guide the use of these medications. Randomized control trials are necessary to evaluate their efficacies.

Determination of Sensitivity and Specificity of Electrocardiography for Left Ventricular Hypertrophy in a Large, Diverse Patient Population.

BACKGROUND: Electrocardiography (ECG) is poorly sensitive, but highly specific for the diagnosis of left ventricular hypertrophy. However, previous studies documenting this were small and lacked patient diversity. Furthermore, little is known about the impact of patient characteristics on the sensitivity and specificity of ECG for left ventricular hypertrophy. To address this issue, the present study was conducted to ascertain the sensitivity and specificity of ECG for left ventricular hypertrophy in a large, diverse patient population. METHODS: We performed a retrospective cohort study using ECG and echocardiography (ECHO) data from a large metropolitan health system. All patients had one ECG and ECHO on file, obtained within 1 week of each other. Sensitivity and specificity of ECG for left ventricular hypertrophy were determined by comparing results from the MUSE® 12-SL (GE Healthcare, Chicago, IL) computer-generated algorithm for ECG to ECHO left ventricular mass index. Subgroup analyses of individual patient characteristics were performed with corresponding chi-squared analyses to determine significance. RESULTS: A total of 13,960 subjects were included in the study. The typical subject was 60 years of age or older, female, overweight, and hypertensive, and demonstrated low socioeconomic status. The sensitivity and specificity of ECG for left ventricular hypertrophy in the overall cohort were 30.7% and 84.4%, respectively, with multiple patient characteristics influencing these results. CONCLUSIONS: This is the first study to confirm the sensitivity and specificity of ECG for left ventricular hypertrophy in a large, diverse patient population with significant minority representation. Furthermore, although these statistical measures are influenced by patient characteristics, such differences are likely not clinically significant.