Race, Genotype, and Prognosis in Black Patients With Transthyretin Cardiac Amyloidosis.

Previous studies suggest worse outcomes in patients with variant transthyretin cardiac amyloidosis (ATTR-CA) because of valine-to-isoleucine substitution at Position 122 (V122I) (ATTRv-CA) compared with patients with wild-type (WT) disease (ATTRwt-CA). Given V122I is almost exclusively found in Black patients, it is unclear if this is attributable to the biology of genotype or racial differences. Patients with ATTR-CA diagnosed between January 2001 and August 2021 were characterized into 3 categories: (1) White with ATTRwt-CA (White-WT); (2) Black with V122I ATTRv-CA (Black-V122I), and (3) Black with ATTRwt-CA (Black-WT). Event-free survival (composite of death, left ventricular assist device, or cardiac transplant) was evaluated using univariable and multivariable analyses over a median follow-up of 1.6 (0.7 to 2.90) years. Of 694 ATTR-CA patients, 502 (72%) were White-WT, 139 Black-V122I (20%), and 53 Black-WT (8%). Notably, 28% of Black patients with ATTR-CA had WT disease and not the V122I variant. Using multivariable modeling to adjust for several prognostic features, Black-V122I had higher risk of the composite adverse outcome compared with a grouped cohort of patients with WT disease (White-WT and Black-WT) (hazard ratio [HR] 1.82, confidence interval [CI] 1.30-2.56, p < 0.001). Furthermore, the Black cohort as a whole (Black-V122I and Black-WT) demonstrated greater risk of adverse outcomes compared with White-WT (HR 1.63, CI 1.19-2.24, p = 0.002). Black-V122I had greater risk of the primary end point compared with White-WT (HR 1.80, CI 1.27-2.56, p = 0.001). Black patients with ATTR-CA have worse event-free survival than White-WT despite risk adjustment. However, it remains unclear whether this is driven by differences in race or genotype given the smaller number of Black-WT patients. Approximately one-quarter of Black patients had WT, of which a greater proportion were female compared with White-WT.

Changes in Left Ventricular Ejection Fraction and Clinical Trajectories of Transthyretin Cardiac Amyloidosis with Systolic Dysfunction.

BACKGROUND: Transthyretin cardiac amyloidosis (ATTR-CM) is classically thought of as a progressive disease with preserved systolic function. The longitudinal clinical trajectories of ATTR-CM with impaired left ventricular ejection fraction (LVEF) remain unclear. METHODS: This is a single-center retrospective cohort study of consecutive patients with ATTR-CM who underwent two or more echocardiograms with baseline LVEF < 50%. Patients were stratified according to the presence of ≥5% change in LVEF. A Cox proportional hazard model examined hazard of a composite outcome of death, transplant, or LVAD insertion over the two years following diagnosis. RESULTS: In our study cohort of 179 patients, 62 patients (34.6%) experienced an increase in LVEF while 33 (18.4%) experienced a decrease in LVEF. After adjusting for covariates, patients with a decrease in EF experienced increased hazard of death (HR 2.15, 95% CI 1.05-4.40, p = 0.038) compared to those with stable or an increase in LVEF. Changes in LVEF corresponded with significant differences in NT proBNP trajectories, but initial biomarker levels or clinical staging were not predictive of LVEF trajectory. CONCLUSIONS: in ATTR-CM patients with impaired LVEF, over a third demonstrated improved LVEF over time, while those with a decrease in LVEF had worse long-term outcomes.

Multicenter registry and test bed for extended outpatient hemodynamic monitoring: the hemodynamic frontiers in heart failure (HF2) initiative.

Background: Hemodynamic Frontiers in Heart Failure (HF2) is a multicenter academic research consortium comprised of 14 US institutions with mature remote monitoring programs for ambulatory patients with heart failure (HF). The consortium developed a retrospective and prospective registry of patients implanted with a wireless pulmonary artery pressure (PAP) sensor. Goals/aims: HF2 registry collects demographic, clinical, laboratory, echocardiographic (ECHO), and hemodynamic data from patients with PAP sensors. The aims of HF2 are to advance understanding of HF and to accelerate development of novel diagnostic and therapeutic innovations. Methods: HF2 includes adult patients implanted with a PAP sensor as per FDA indications (New York Heart Association (NYHA) Class III HF functional class with a prior hospitalization, or patients with NYHA Class II or brain natriuretic peptide (BNP) elevation without hospitalization) at a HF2 member site between 1/1/19 to present. HF2 registry is maintained at University of Kansas Medical Center (KUMC). The registry was approved by the institutional review board (IRB) at all participating institutions with required data use agreements. Institutions report data into the electronic registry database using REDCap, housed at KUMC. Results: This initial data set includes 254 patients implanted from the start of 2019 until May 2023. At time of device implant, the cohort average age is 73 years old, 59.8% are male, 72% have NYHA Class III HF, 40% have left ventricular ejection fraction (LVEF) < 40%, 35% have LVEF > 50%, mean BNP is 560 pg/ml, mean N-Terminal pro-BNP (NTproBNP) is 5,490 pg/ml, mean creatinine is 1.65 mg/dl. Average baseline hemodynamics at device implant are right atrial pressure (RAP) of 11 mmHg, pulmonary artery systolic pressure (PASP) of 47 mmHg, pulmonary artery diastolic pressure (PADP) 21 mmHg, mean pulmonary artery pressure (mPAP) of 20 mmHg, pulmonary capillary wedge pressure (PCWP) of 19 mmHg, cardiac output (CO) of 5.3 L/min, and cardiac index (CI) of 2.5 L/min/m2. Conclusion: A real-world registry of patients implanted with a PAP sensor enables long-term evaluation of hemodynamic and clinic outcomes in highly-phenotyped ambulatory HF patients, and creates a unique opportunity to validate and test novel diagnostic and therapeutic approaches to HF.

Impact of exercise on pulmonary artery pressure in patients with heart failure using an ambulatory pulmonary artery pressure monitor.

Background: In this multicenter prospective study, we explored the relationship between pulmonary artery pressure (PAP) at rest and in response to a 6-min walk test (6MWT) in ambulatory patients with heart failure (HF) with an implantable PAP sensor (CardioMEMS, Abbott). Methods: Between 5/2019 and 2/2021, HF patients with a CardioMEMS sensor were recruited from seven sites. PAP was recorded in the supine and seated position at rest and in the seated position immediately post-exercise. Results: In our cohort of 66 patients, mean age was 70 ± 12 years, 67% male, left ventricular ejection fraction (LVEF) < 50% in 53%, mean 6MWT distance was 277 ± 95 meters. Resting seated PAPs were 31 ± 15 mmHg (systolic), 13 ± 8 mmHg (diastolic), and 20 ± 11 mmHg (mean). The pressures were lower in the seated rather than the supine position. After 6MWT, the pressures increased to PAP systolic 37 ± 19 mmHg (p < 0.0001), diastolic 15 ± 10 mmHg (p = 0.006), and mean 24 ± 13 mmHg (p < 0.0001). Patients with elevated PAP diastolic at rest (>15 mmHg) demonstrated a greater increase in post-exercise PAP. Conclusion: The measurement of PAP with CardioMEMS is feasible immediately post-exercise. Despite being well-managed, patients had severely limited functional capacity. We observed a significant increase in PAP with ambulation which was greater in patients with higher baseline pressures.

Convolution Neural Network Algorithm for Shockable Arrhythmia Classification Within a Digitally Connected Automated External Defibrillator.

Background Diagnosis of shockable rhythms leading to defibrillation remains integral to improving out-of-hospital cardiac arrest outcomes. New machine learning techniques have emerged to diagnose arrhythmias on ECGs. In out-of-hospital cardiac arrest, an algorithm within an automated external defibrillator is the major determinant to deliver defibrillation. This study developed and validated the performance of a convolution neural network (CNN) to diagnose shockable arrhythmias within a novel, miniaturized automated external defibrillator. Methods and Results There were 26 464 single-lead ECGs that comprised the study data set. ECGs of 7-s duration were retrospectively adjudicated by 3 physician readers (N=18 total readers). After exclusions (N=1582), ECGs were divided into training (N=23 156), validation (N=721), and test data sets (N=1005). CNN performance to diagnose shockable and nonshockable rhythms was reported with area under the receiver operating characteristic curve analysis, F1, and sensitivity and specificity calculations. The duration for the CNN to output was reported with the algorithm running within the automated external defibrillator. Internal and external validation analyses included CNN performance among arrhythmias, often mistaken for shockable rhythms, and performance among ECGs modified with noise to mimic artifacts. The CNN algorithm achieved an area under the receiver operating characteristic curve of 0.995 (95% CI, 0.990-1.0), sensitivity of 98%, and specificity of 100% to diagnose shockable rhythms. The F1 scores were 0.990 and 0.995 for shockable and nonshockable rhythms, respectively. After input of a 7-s ECG, the CNN generated an output in 383±29 ms (total time of 7.383 s). The CNN outperformed adjudicators in classifying atrial arrhythmias as nonshockable (specificity of 99.3%-98.1%) and was robust against noise artifacts (area under the receiver operating characteristic curve range, 0.871-0.999). Conclusions We demonstrate high diagnostic performance of a CNN algorithm for shockable and nonshockable rhythm arrhythmia classifications within a digitally connected automated external defibrillator. Registration URL: https://clinicaltrials.gov/ct2/show/NCT03662802; Unique identifier: NCT03662802.

A Review of Current and Evolving Imaging Techniques in Cardiac Amyloidosis.

Purpose of review: Establishing an early, efficient diagnosis for cardiac amyloid (CA) is critical to avoiding adverse outcomes. We review current imaging tools that can aid early diagnosis, offer prognostic information, and possibly track treatment response in CA. Recent findings: There are several current conventional imaging modalities that aid in the diagnosis of CA including electrocardiography, echocardiography, bone scintigraphy, cardiac computed tomography (CT), and cardiac magnetic resonance (CMR) imaging. Advanced imaging techniques including left atrial and right ventricular strain, and CMR T1 and T2 mapping as well as ECV quantification may provide alternative non-invasive means for diagnosis, more granular prognostication, and the ability to track treatment response. Summary: Leveraging a multimodal imaging toolbox is integral to the early diagnosis of CA; however, it is important to understand the unique role and limitations posed by each modality. Ongoing studies are needed to help identify imaging markers that will lead to an enhanced ability to diagnose, subtype and manage this condition.

Multicenter validation of a machine learning phase space electro-mechanical pulse wave analysis to predict elevated left ventricular end diastolic pressure at the point-of-care.

BACKGROUND: Phase space is a mechanical systems approach and large-scale data representation of an object in 3-dimensional space. Whether such techniques can be applied to predict left ventricular pressures non-invasively and at the point-of-care is unknown. OBJECTIVE: This study prospectively validated a phase space machine-learned approach based on a novel electro-mechanical pulse wave method of data collection through orthogonal voltage gradient (OVG) and photoplethysmography (PPG) for the prediction of elevated left ventricular end diastolic pressure (LVEDP). METHODS: Consecutive outpatients across 15 US-based healthcare centers with symptoms suggestive of coronary artery disease were enrolled at the time of elective cardiac catheterization and underwent OVG and PPG data acquisition immediately prior to angiography with signals paired with LVEDP (IDENTIFY; NCT #03864081). The primary objective was to validate a ML algorithm for prediction of elevated LVEDP using a definition of ≥25 mmHg (study cohort) and normal LVEDP ≤ 12 mmHg (control cohort), using AUC as the measure of diagnostic accuracy. Secondary objectives included performance of the ML predictor in a propensity matched cohort (age and gender) and performance for an elevated LVEDP across a spectrum of comparative LVEDP (<12 through 24 at 1 mmHg increments). Features were extracted from the OVG and PPG datasets and were analyzed using machine-learning approaches. RESULTS: The study cohort consisted of 684 subjects stratified into three LVEDP categories, ≤12 mmHg (N = 258), LVEDP 13-24 mmHg (N = 347), and LVEDP ≥25 mmHg (N = 79). Testing of the ML predictor demonstrated an AUC of 0.81 (95% CI 0.76-0.86) for the prediction of an elevated LVEDP with a sensitivity of 82% and specificity of 68%, respectively. Among a propensity matched cohort (N = 79) the ML predictor demonstrated a similar result AUC 0.79 (95% CI: 0.72-0.8). Using a constant definition of elevated LVEDP and varying the lower threshold across LVEDP the ML predictor demonstrated and AUC ranging from 0.79-0.82. CONCLUSION: The phase space ML analysis provides a robust prediction for an elevated LVEDP at the point-of-care. These data suggest a potential role for an OVG and PPG derived electro-mechanical pulse wave strategy to determine if LVEDP is elevated in patients with symptoms suggestive of cardiac disease.

Identifying novel phenotypes of elevated left ventricular end diastolic pressure using hierarchical clustering of features derived from electromechanical waveform data.

Introduction: Elevated left ventricular end diastolic pressure (LVEDP) is a consequence of compromised left ventricular compliance and an important measure of myocardial dysfunction. An algorithm was developed to predict elevated LVEDP utilizing electro-mechanical (EM) waveform features. We examined the hierarchical clustering of selected features developed from these EM waveforms in order to identify important patient subgroups and assess their possible prognostic significance. Materials and methods: Patients presenting with cardiovascular symptoms (N = 396) underwent EM data collection and direct LVEDP measurement by left heart catheterization. LVEDP was classified as non-elevated ( ≤ 12 mmHg) or elevated (≥25 mmHg). The 30 most contributive features to the algorithm output were extracted from EM data and input to an unsupervised hierarchical clustering algorithm. The resultant dendrogram was divided into five clusters, and patient metadata overlaid. Results: The cluster with highest LVEDP (cluster 1) was most dissimilar from the lowest LVEDP cluster (cluster 5) in both clustering and with respect to clinical characteristics. In contrast to the cluster demonstrating the highest percentage of elevated LVEDP patients, the lowest was predominantly non-elevated LVEDP, younger, lower BMI, and males with a higher rate of significant coronary artery disease (CAD). The next adjacent cluster (cluster 2) to that of the highest LVEDP (cluster 1) had the second lowest LVEDP of all clusters. Cluster 2 differed from Cluster 1 primarily based on features extracted from the electrical data, and those that quantified predictability and variability of the signal. There was a low predictability and high variability in the highest LVEDP cluster 1, and the opposite in adjacent cluster 2. Conclusion: This analysis identified subgroups of patients with varying degrees of LVEDP elevation based on waveform features. An approach to stratify movement between clusters and possible progression of myocardial dysfunction may include changes in features that differentiate clusters; specifically, reductions in electrical signal predictability and increases in variability. Identification of phenotypes of myocardial dysfunction evidenced by elevated LVEDP and knowledge of factors promoting transition to clusters with higher levels of left ventricular filling pressures could permit early risk stratification and improve patient selection for novel therapeutic interventions.

Cardiac amyloidosis: an update on diagnosis, current therapy, and future directions.

PURPOSE OF REVIEW: This review aims to serve as a practical guide for differentiating the two most common forms of cardiac amyloidosis, as well as reviewing the approach to diagnosis and management, particularly as it pertains to transthyretin cardiac amyloidosis. RECENT FINDINGS: Emerging literature continues to unravel new understandings and challenges in the field of cardiac amyloidosis. Although cardiac amyloidosis has historically been thought of as a 'zebra diagnosis', current evidence has shown that this is a common cause of heart failure. Furthermore, it has become increasingly apparent that earlier diagnosis leads to improved outcomes and quality of life for patients. SUMMARY: By leveraging an understanding of the pathophysiology leading to amyloid fibril formation, new drug therapeutics are under investigation as promising candidates for the treatment of amyloid cardiomyopathy.

Framework for Digital Health Phenotypes in Heart Failure: From Wearable Devices to New Sensor Technologies.

Consider these 2 scenarios: Two individuals with heart failure (HF) have recently established with your clinic and followed for medical management and risk stratification. One is a 62-year-old man with nonischemic cardiomyopathy due to viral myocarditis, an ejection fraction (EF) of 40%, occasional rate-limiting dyspnea, and comorbidities of atrial fibrillation and hypertension. The other is a 75-year-old woman with ischemic cardiomyopathy, an EF of 35%, a prior hospitalization 6 months ago, and persistent symptoms of edema and orthopnea. Both have expressed interest in remote patient monitoring (RPM) with wearable and digital health devices that are commercially available such as a smartwatch-ECG, weight scales, and blood pressure monitoring technologies. While there is enthusiasm from both patients and their clinical teams to engage in a technology-driven approach to care, important questions arise such as "What are the patient requirements for participation in digital health programs?", "Can we anticipate improvements in HF status and lower the risk of future HF events including hospitalizations?", "Do the same type of devices in different patients provide accurate information on physiologic changes toward individualized risk assessments?", and "What are the systematic approaches to integrate digital health workflows and datasets from RPM into clinical HF programs?". Given the importance of such questions, embracing new technologies, as a core competency of a modern health care system requires a deeper understanding of how effective digital health programs can be designed to meet the needs of patients and their clinical teams. In this review, we propose a new framework of "Digital Phenotypes in HF" for how new devices and sensors and their respective datasets can be used to guide treatment and to predict disease trajectories within the heterogeneity of HF. Our objectives are to generate a systematic approach to evaluate digital health devices as they relate to the next phase of RPM in HF, to critically analyze the literature, and to apply the lessons learned from digital devices through present-day, real-world evidence examples.

Cardiac Amyloidosis Screening at Trigger Finger Release Surgery.

Cardiac amyloidosis is often preceded by orthopedic manifestations such as carpal tunnel syndrome, and 10% of patients who underwent idiopathic carpal tunnel release surgery will have biopsy-confirmed amyloid deposits in the tenosynovial sheath. Trigger finger is also commonly reported in patients with amyloidosis and involves the same tendon sheath as carpal tunnel syndrome, but the prevalence of amyloid deposition is unclear. This prospective cross-sectional study enrolled 100 patients aged ≥50 years at the time of surgery for idiopathic trigger finger. Patients underwent release surgery, and a sample of the tenosynovium of the affected finger was excised, stained with Congo red, and subtyped with mass spectrometry if amyloid was demonstrated. Further cardiac evaluation was performed in patients with amyloid deposition. Of the 100 patients (mean age 65.5 ± 8.1 years) enrolled, only 2 demonstrated amyloid deposits on Congo red staining. One patient with previous proteinuric kidney disease had fibrinogen A α-chain amyloidosis, and the other patient had untyped amyloidosis. Neither patient had cardiac involvement. A total of 13 of the 100 patients underwent concomitant carpal tunnel release surgery, and 2 of these patients had amyloid deposits in the carpal tunnel with "false-negative" samples from the trigger finger tenosynovium. In conclusion, biopsy during trigger finger release surgery demonstrated a 2% yield for amyloidosis, which is significantly lower than the previously published yield of 10% during carpal tunnel release surgery. This observation has important implications for the development of diagnostic algorithms to screen patients for amyloidosis during orthopedic operations.

Disparities in heart and lung transplantation.

PURPOSE OF REVIEW: Cardiothoracic transplantation is the definitive therapy for end-stage heart and lung disease. In service to this population, disparities in access and care must be simultaneously understood and addressed. RECENT FINDINGS: There are sex, race, geographic, age, and underlying disease disparities in both heart and lung transplantation. Women have reduced waitlist survival but improved posttransplant survival when compared with men for both heart and lung transplantation. Black patients have worse outcome compared with other races postheart transplant. Geographic disparities impact the likelihood of receiving heart or lung transplant and the growing number of patients with advanced age seeking transplant complicates discussions on survival benefit. Finally, underlying disease has affected outcomes for both heart and lung transplant and now are incorporated into the allocation system. SUMMARY: Though heart and lung transplantation have several existing disparities, it remains to be seen how advancements in medical technology, changes in donor organ allocation policies, and growing experience in patient selection will impact these concerns.

A Critical Review of Hemodynamically Guided Therapy for Cardiogenic Shock: Old Habits Die Hard.

PURPOSE OF REVIEW: Here, we review the importance of using hemodynamic data to guide therapy and risk stratification in cardiogenic shock as well as the various definitions of this syndrome that have been used in prior studies. Furthermore, we provide perspective regarding the controversy surrounding pulmonary artery (PA) catheter use as well as current society guidelines and scientific statements. Lastly, we review the technical aspects for accurate interpretation of data of cardiogenic shock. RECENT FINDINGS: More recent studies specifically evaluating cardiogenic shock patients have shown higher mortality when PA catheters were not used. Furthermore, initiatives are underway to develop more standardized definitions of cardiogenic shock, including the SCAI Shock Classification Scheme. Only by having a standardized fashion of conveying severity of shock will we be able to more systematically study this patient population and improve outcomes moving forward. SUMMARY: PA catheters are critical to the prognostication and management of a subset of patients with cardiopulmonary disease, particularly in those with pulmonary hypertension, cardiogenic shock, or requiring mechanical circulatory support or undergoing evaluation for advanced heart failure therapies.

Predicting cardiac disease from interactions of simultaneously-acquired hemodynamic and cardiac signals.

BACKGROUND AND OBJECTIVE: Coronary artery disease (CAD) and heart failure are the most common cardiovascular diseases. Non-invasive diagnostic testing for CAD requires radiation, heart rate acceleration, and imaging infrastructure. Early detection of left ventricular dysfunction is critical in heart failure management, the best measure of which is an elevated left ventricular end-diastolic pressure (LVEDP) that can only be measured using invasive cardiac catheterization. There exists a need for non-invasive, safe, and fast diagnostic testing for CAD and elevated LVEDP. This research employs nonlinear dynamics to assess for significant CAD and elevated LVEDP using non-invasively acquired photoplethysmographic (PPG) and three-dimensional orthogonal voltage gradient (OVG) signals. PPG (variations of the blood volume perfusing the tissue) and OVG (mechano-electrical activity of the heart) signals represent the dynamics of the cardiovascular system. METHODS: PPG and OVG were simultaneously acquired from two cohorts, (i) symptomatic subjects that underwent invasive cardiac catheterization, the gold standard test (408 CAD positive with stenosis≥ 70% and 186 with LVEDP≥ 20 mmHg) and (ii) asymptomatic healthy controls (676). A set of Poincaré-based synchrony features were developed to characterize the interactions between the OVG and PPG signals. The extracted features were employed to train machine learning models for CAD and LVEDP. Five-fold cross-validation was used and the best model was selected based on the average area under the receiver operating characteristic curve (AUC) across 100 runs, then assessed using a hold-out test set. RESULTS: The Elastic Net model developed on the synchrony features can effectively classify CAD positive subjects from healthy controls with an average validation AUC=0.90±0.03 and an AUC= 0.89 on the test set. The developed model for LVEDP can discriminate subjects with elevated LVEDP from healthy controls with an average validation AUC=0.89±0.03 and an AUC=0.89 on the test set. The feature contributions results showed that the selection of a proper registration point for Poincaré analysis is essential for the development of predictive models for different disease targets. CONCLUSIONS: Nonlinear features from simultaneously-acquired signals used as inputs to machine learning can assess CAD and LVEDP safely and accurately with an easy-to-use, portable device, utilized at the point-of-care without radiation, contrast, or patient preparation.

Rare survival of left ventricular free wall rupture following myocardial infarction.

Myocardial free wall rupture is a rare but usually fatal complication of acute myocardial infarction (MI) especially if it occurs out of hospital and occurs in 2-4% of patients who suffer from acute MI. Rapid diagnosis is essential but not always easy as diagnostic tests may be inconclusive. In this case report authors examine a rare and unique patient survival after left ventricular free wall rupture following MI. The patient developed chest pain and hypotension in the hospital and was taken directly to the catheterization laboratory where a diagnostic angiogram showed a high-grade occlusion of a very small marginal branch, fluoroscopy demonstrated a large pericardial effusion, which was drained then auto transfused back to the patient using a femoral vein sheath. Rapid diagnostic testing including transesophageal echocardiography with Definity, transthoracic echocardiography, aortography and left ventriculography were all negative for dissection and rupture. Despite the negative diagnostic test, a high index of suspicion for rupture led to urgent surgical exploration where a large 4-cm hole was found in the lateral wall. Repair was successful and the patient left the hospital about several weeks later.

Incidence of Cardiac Implantable Electronic Device Complications in Patients With Left Ventricular Assist Devices.

OBJECTIVES: The objective of this study was to describe the risk of cardiac implantable electronic devices (CIEDs) complications in patients with left ventricular assist devices (LVADs). BACKGROUND: Patients with LVADs are predisposed to ventricular arrhythmias and frequently have CIEDs before receiving their LVAD. However, the role of CIED procedures such as generator changes (GC) are unclear in this population, given the potential complications of bleeding and infection. METHODS: This was a retrospective, multicenter study from January 1, 2012, to September 30, 2018. All patients with LVADs were screened and those who had a CIED GC, implantation, or revision were included in the study and followed until December 31, 2018. RESULTS: A total of 179 patients across 6 centers had a CIED procedure after LVAD implantation. The mean age was 59.5 ± 13.4, with the cohort comprising mostly men (78%), destination LVAD therapy (53.8%), and GC (66%). The 30-day primary composite endpoint of hematoma or device infection occurred in 34 (19%) patients. The secondary endpoints of rehospitalization within 30 days and appropriate device therapy during follow-up occurred in 40 (22%) and 42 (24%) patients respectively. Of the 126 patients without previous device therapy, 14.3% received appropriate therapy during follow-up. CONCLUSIONS: In this large, multicenter cohort, we report the incidence of complications for CIED procedures in the LVAD population; specifically, LVAD patients are at increased risk of pocket hematomas, without downstream risk of infection, and do experience a high rate of appropriate device therapies.