Prospective validation of a seizure diary forecasting falls short.

OBJECTIVE: Recently, a deep learning artificial intelligence (AI) model forecasted seizure risk using retrospective seizure diaries with higher accuracy than random forecasts. The present study sought to prospectively evaluate the same algorithm. METHODS: We recruited a prospective cohort of 46 people with epilepsy; 25 completed sufficient data entry for analysis (median = 5 months). We used the same AI method as in our prior study. Group-level and individual-level Brier Skill Scores (BSSs) compared random forecasts and simple moving average forecasts to the AI. RESULTS: The AI had an area under the receiver operating characteristic curve of .82. At the group level, the AI outperformed random forecasting (BSS = .53). At the individual level, AI outperformed random in 28% of cases. At the group and individual level, the moving average outperformed the AI. If pre-enrollment (nonverified) diaries (with presumed underreporting) were included, the AI significantly outperformed both comparators. Surveys showed most did not mind poor-quality LOW-RISK or HIGH-RISK forecasts, yet 91% wanted access to these forecasts. SIGNIFICANCE: The previously developed AI forecasting tool did not outperform a very simple moving average forecasting in this prospective cohort, suggesting that the AI model should be replaced.

Prospective validation of a seizure diary forecasting falls short.

OBJECTIVE: Recently, a deep learning AI model forecasted seizure risk using retrospective seizure diaries with higher accuracy than random forecasts. The present study sought to prospectively evaluate the same algorithm. METHODS: We recruited a prospective cohort of 46 people with epilepsy; 25 completed sufficient data entry for analysis (median 5 months). We used the same AI method as in our prior study. Group-level and individual-level Brier Skill Scores (BSS) compared random forecasts and simple moving average forecasts to the AI. RESULTS: The AI had an AUC of 0.82. At the group level, the AI outperformed random forecasting (BSS=0.53). At the individual level, AI outperformed random in 28% of cases. At the group and individual level, the moving average outperformed the AI. If pre-enrollment (non-verified) diaries (with presumed under-reporting) were included, the AI significantly outperformed both comparators. Surveys showed most did not mind poor quality LOW-RISK or HIGH-RISK forecasts, yet 91% wanted access to these forecasts. SIGNIFICANCE: The previously developed AI forecasting tool did not outperform a very simple moving average forecasting this prospective cohort, suggesting that the AI model should be replaced.

Optimisation of the air fraction correction for lung PET/CT: addressing resolution mismatch.

BACKGROUND: Increased pulmonary [Formula: see text]F-FDG metabolism in patients with idiopathic pulmonary fibrosis, and other forms of diffuse parenchymal lung disease, can predict measurements of health and lung physiology. To improve PET quantification, voxel-wise air fractions (AF) determined from CT can be used to correct for variable air content in lung PET/CT. However, resolution mismatches between PET and CT can cause artefacts in the AF-corrected image. METHODS: Three methodologies for determining the optimal kernel to smooth the CT are compared with noiseless simulations and non-TOF MLEM reconstructions of a patient-realistic digital phantom: (i) the point source insertion-and-subtraction method, [Formula: see text]; (ii) AF-correcting with varyingly smoothed CT to achieve the lowest RMSE with respect to the ground truth (GT) AF-corrected volume of interest (VOI), [Formula: see text]; iii) smoothing the GT image to match the reconstruction within the VOI, [Formula: see text]. The methods were evaluated both using VOI-specific kernels, and a single global kernel optimised for the six VOIs combined. Furthermore, [Formula: see text] was implemented on thorax phantom data measured on two clinical PET/CT scanners with various reconstruction protocols. RESULTS: The simulations demonstrated that at [Formula: see text] iterations (200 i), the kernel width was dependent on iteration number and VOI position in the lung. The [Formula: see text] method estimated a lower, more uniform, kernel width in all parts of the lung investigated. However, all three methods resulted in approximately equivalent AF-corrected VOI RMSEs (<10%) at [Formula: see text]200i. The insensitivity of AF-corrected quantification to kernel width suggests that a single global kernel could be used. For all three methodologies, the computed global kernel resulted in an AF-corrected lung RMSE <10%  at [Formula: see text]200i, while larger lung RMSEs were observed for the VOI-specific kernels. The global kernel approach was then employed with the [Formula: see text] method on measured data. The optimally smoothed GT emission matched the reconstructed image well, both within the VOI and the lung background. VOI RMSE was <10%, pre-AFC, for all reconstructions investigated. CONCLUSIONS: Simulations for non-TOF PET indicated that around 200i were needed to approach image resolution stability in the lung. In addition, at this iteration number, a single global kernel, determined from several VOIs, for AFC, performed well over the whole lung. The [Formula: see text] method has the potential to be used to determine the kernel for AFC from scans of phantoms on clinical scanners.

Transfemoral tricuspid valve replacement and one-year outcomes: the TRISCEND study.

BACKGROUND AND AIMS: For patients with symptomatic, severe tricuspid regurgitation (TR), early results of transcatheter tricuspid valve (TV) intervention studies have shown significant improvements in functional status and quality of life associated with right-heart reverse remodelling. Longer-term follow-up is needed to confirm sustained improvements in these outcomes. METHODS: The prospective, single-arm, multicentre TRISCEND study enrolled 176 patients to evaluate the safety and performance of transcatheter TV replacement in patients with ≥moderate, symptomatic TR despite medical therapy. Major adverse events, reduction in TR grade and haemodynamic outcomes by echocardiography, and clinical, functional, and quality-of-life parameters are reported to one year. RESULTS: Enrolled patients were 71.0% female, mean age 78.7 years, 88.0% ≥ severe TR, and 75.4% New York Heart Association classes III-IV. Tricuspid regurgitation was reduced to ≤mild in 97.6% (P < .001), with increases in stroke volume (10.5 ± 16.8 mL, P < .001) and cardiac output (0.6 ± 1.2 L/min, P < .001). New York Heart Association class I or II was achieved in 93.3% (P < .001), Kansas City Cardiomyopathy Questionnaire score increased by 25.7 points (P < .001), and six-minute walk distance increased by 56.2 m (P < .001). All-cause mortality was 9.1%, and 10.2% of patients were hospitalized for heart failure. CONCLUSIONS: In an elderly, highly comorbid population with ≥moderate TR, patients receiving transfemoral EVOQUE transcatheter TV replacement had sustained TR reduction, significant increases in stroke volume and cardiac output, and high survival and low hospitalization rates with improved clinical, functional, and quality-of-life outcomes to one year. Funded by Edwards Lifesciences, TRISCEND ClinicalTrials.gov number, NCT04221490.

Hybrid energy and angle dispersive X-ray diffraction computed tomography.

Pixelated energy resolving detectors enable acquisition of X-ray diffraction (XRD) signals using a hybrid energy- and angle- dispersive technique, potentially paving the way for the development of novel benchtop XRD imaging or computed tomography (XRDCT) systems, utilising readily available polychromatic X-ray sources. In this work, a commercially available pixelated cadmium telluride (CdTe) detector, HEXITEC (High Energy X-ray Imaging Technology), was used to demonstrate such an XRDCT system. Specifically, a novel fly-scan technique was developed and compared to the established step-scan technique, reducing the total scan time by 42% while improving the spatial resolution, material contrast and therefore the material classification.

Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020.

As of January 2021, Australia had effectively controlled local transmission of COVID-19 despite a steady influx of imported cases and several local, but contained, outbreaks in 2020. Throughout 2020, state and territory public health responses were informed by weekly situational reports that included an ensemble forecast of daily COVID-19 cases for each jurisdiction. We present here an analysis of one forecasting model included in this ensemble across the variety of scenarios experienced by each jurisdiction from May to October 2020. We examine how successfully the forecasts characterised future case incidence, subject to variations in data timeliness and completeness, showcase how we adapted these forecasts to support decisions of public health priority in rapidly-evolving situations, evaluate the impact of key model features on forecast skill, and demonstrate how to assess forecast skill in real-time before the ground truth is known. Conditioning the model on the most recent, but incomplete, data improved the forecast skill, emphasising the importance of developing strong quantitative models of surveillance system characteristics, such as ascertainment delay distributions. Forecast skill was highest when there were at least 10 reported cases per day, the circumstances in which authorities were most in need of forecasts to aid in planning and response.

Patient experiences of implantable cardiac monitoring in hypertrophic cardiomyopathy: an exploratory study.

AIMS: Hypertrophic cardiomyopathy (HCM) is the most common inherited heart disease. Insertable cardiac monitors (ICMs) are increasingly used in this population to provide closer monitoring, with the potential for notification systems. However, little is known regarding the psychological impact this information may have on patients. The Abbott Confirm Rx™ ICM has the capability of connecting to the patient's smartphone to enable active participation in their care, as well as two-way communication between the patient and their care providers. This study aimed to explore individuals' experiences of having a smartphone-enabled ICM to monitor for arrhythmias in HCM. METHODS AND RESULTS: Semi-structured interviews were conducted with 10 participants. Utilizing a grounded theory approach, the interview guide was modified based on emerging themes throughout the study. Reflexive thematic analysis was applied to categorize interview data into codes and overacting themes, with each interview independently coded by two study members. Analysis revealed three key themes: (i) psychological impact, (ii) educational needs, and (iii) technology expectations. Participants reported that receiving feedback from ICM transmissions resulted in improved symptom clarity, providing reassurance, and aiding implantable cardioverter defibrillator decision-making. Some participants reported uncertainty regarding when to send manual transmissions. Lastly, participants reported the app interface did not meet expectations with regard to the amount of data available for patients. CONCLUSION: Overall, utilizing a smartphone app to facilitate two-way communication of ICM transmissions was well accepted. Future directions include addressing gaps in educational needs and improvements in the patient interface with increased access to data.

Development of a core outcome set for quality of life for adults with drug-resistant epilepsy: A multistakeholder Delphi consensus study.

OBJECTIVE: In 2017, the American Academy of Neurology (AAN) convened the AAN Quality Measurement Set working group to define the improvement and maintenance of quality of life (QOL) as a key outcome measure in epilepsy clinical practice. A core outcome set (COS), defined as an accepted, standardized set of outcomes that should be minimally measured and reported in an area of health care research and practice, has not previously been defined for QOL in adult epilepsy. METHODS: A cross-sectional Delphi consensus study was employed to attain consensus from patients and caregivers on the QOL outcomes that should be minimally measured and reported in epilepsy clinical practice. Candidate items were compiled from QOL scales recommended by the AAN 2017 Quality Measurement Set. Inclusion criteria to participate in the Delphi study were adults with drug-resistant epilepsy diagnosed by a physician, no prior diagnosis of psychogenic nonepileptic seizures or a cognitive and/or developmental disability, or caregivers of patients meeting these criteria. RESULTS: A total of 109 people satisfied inclusion/exclusion criteria and took part in Delphi Round 1 (patients, n = 95, 87.2%; caregivers, n = 14, 12.8%), and 55 people from Round 1 completed Round 2 (patients, n = 43, 78.2%; caregivers, n = 12, 21.8%). One hundred three people took part in the final consensus round. Consensus was attained by patients/caregivers on a set of 36 outcomes that should minimally be included in the QOL COS. Of these, 32 of the 36 outcomes (88.8%) pertained to areas outside of seizure frequency and severity. SIGNIFICANCE: Using patient-centered Delphi methodology, this study defines the first COS for QOL measurement in clinical practice for adults with drug-resistant epilepsy. This set highlights the diversity of factors beyond seizure frequency and severity that impact QOL in epilepsy.

BAYESIAN NON-HOMOGENEOUS HIDDEN MARKOV MODEL WITH VARIABLE SELECTION FOR INVESTIGATING DRIVERS OF SEIZURE RISK CYCLING.

A major issue in the clinical management of epilepsy is the unpredictability of seizures. Yet, traditional approaches to seizure forecasting and risk assessment in epilepsy rely heavily on raw seizure frequencies, which are a stochastic measurement of seizure risk. We consider a Bayesian non-homogeneous hidden Markov model for unsupervised clustering of zero-inflated seizure count data. The proposed model allows for a probabilistic estimate of the sequence of seizure risk states at the individual level. It also offers significant improvement over prior approaches by incorporating a variable selection prior for the identification of clinical covariates that drive seizure risk changes and accommodating highly granular data. For inference, we implement an efficient sampler that employs stochastic search and data augmentation techniques. We evaluate model performance on simulated seizure count data. We then demonstrate the clinical utility of the proposed model by analyzing daily seizure count data from 133 patients with Dravet syndrome collected through the Seizure Tracker™ system, a patient-reported electronic seizure diary. We report on the dynamics of seizure risk cycling, including validation of several known pharmacologic relationships. We also uncover novel findings characterizing the presence and volatility of risk states in Dravet syndrome, which may directly inform counseling to reduce the unpredictability of seizures for patients with this devastating cause of epilepsy.

A Bayesian switching linear dynamical system for estimating seizure chronotypes.

Epilepsy is a disorder characterized by paroxysmal transitions between multistable states. Dynamical systems have been useful for modeling the paroxysmal nature of seizures. At the same time, intracranial electroencephalography (EEG) recordings have recently discovered that an electrographic measure of epileptogenicity, interictal epileptiform activity, exhibits cycling patterns ranging from ultradian to multidien rhythmicity, with seizures phase-locked to specific phases of these latent cycles. However, many mechanistic questions about seizure cycles remain unanswered. Here, we provide a principled approach to recast the modeling of seizure chronotypes within a statistical dynamical systems framework by developing a Bayesian switching linear dynamical system (SLDS) with variable selection to estimate latent seizure cycles. We propose a Markov chain Monte Carlo algorithm that employs particle Gibbs with ancestral sampling to estimate latent cycles in epilepsy and apply unsupervised learning on spectral features of latent cycles to uncover clusters in cycling tendency. We analyze the largest database of patient-reported seizures in the world to comprehensively characterize multidien cycling patterns among 1,012 people with epilepsy, spanning from infancy to older adulthood. Our work advances knowledge of cycling in epilepsy by investigating how multidien seizure cycles vary in people with epilepsy, while demonstrating an application of an SLDS to frame seizure cycling within a nonlinear dynamical systems framework. It also lays the groundwork for future studies to pursue data-driven hypothesis generation regarding the mechanistic drivers of seizure cycles.

Annual variation in breeding success in boreal forest grouse: Four decades of monitoring reveals bottom-up drivers to be more important than predation.

Knowledge of the temporal variation in reproductive success and its key driving factors is crucial in predicting animal population persistence. Few studies have examined the effects of a range of explanatory factors operating simultaneously on the same population over a long period. Based on 41 years of monitoring (1979-2019), we tested prevailing hypotheses about drivers of annual variation in breeding success in two sympatric species of boreal forest grouse-the capercaillie (Tetrao urogallus) and the black grouse (T. tetrix)-in a 45 km2 boreal forest landscape. From counts in early August, we measured breeding success (chicks/hen) along with potential determining factors. We formulated five main hypotheses on causes of variation (hen condition, chick weather, chick food, predation, demographic characteristics) and derived 13 associated explanatory variables for analysis. We first tested the five hypotheses separately and then used model selection (AICc) to rank the best predictive models irrespective of hypotheses. Lastly, we used path analysis to illuminate potential causal relationships. Barring demographic characteristics, all hypotheses were supported, most strongly for chick food and predation. Among predictor variables, chick food (insect larvae and bilberry fruit crops), vole and fox abundances, the winter-NAO index, and temperature after hatching, had the strongest effect sizes in both species. Precipitation after hatching had no detectable effect. Model selection indicated bottom-up factors to be more important than predation, but confounding complicated interpretation. Path analysis suggested that the high explanatory power of bilberry fruiting was due not only to its direct positive effect on chick food quality but also to an indirect positive effect on vole abundance, which buffers predation. The two components of breeding success-proportion of hens with broods and number of chicks per brood-were uncorrelated, the former having the strongest effect. The two components had different ecological correlates that often varied asynchronously, resulting in overall breeding success fluctuating around low to moderate levels. Our study highlights the complexity of key explanatory drivers and the importance of considering multiple hypotheses of breeding success. Although chick food appeared to equal or surpass predation in explaining the annual variation in breeding success, predation may still be the overall limiting factor. Comparative and experimental studies of confounded variables (bilberry fruiting, voles, and larvae) are needed to disentangle causes of variation in breeding success of boreal forest grouse.

A phase 1 study of ruxolitinib, steroids and lenalidomide for relapsed/refractory multiple myeloma patients.

Ruxolitinib with lenalidomide and dexamethasone shows anti-myeloma effects in vitro and in vivo. MUC1 leads to lenalidomide resistance in multiple myeloma (MM) cells, and ruxolitinib blocks its expression. Thus, ruxolitinib may restore sensitivity to lenalidomide. A phase I trial was conducted to determine the safety and efficacy of ruxolitinib with lenalidomide and methylprednisolone for patients with relapsed/refractory (RR)MM who had been treated with lenalidomide, steroids and a proteasome inhibitor and showed progressive disease at study entry. A traditional 3 + 3 dose escalation design was used to enroll subjects in four cohorts. Subjects received ruxolitinib twice daily, lenalidomide daily on days 1-21 of a 28 day cycle and methylprednisolone orally every other day. Primary endpoints were safety, clinical benefit rate (CBR) and overall response rate (ORR). Forty-nine patients were enrolled. The median age was 64 years and they had received a median of six prior treatments including lenalidomide and steroids to which 94% were refractory. No dose limiting toxicities occurred. The CBR and ORR were 49% and 36%, respectively. All responding patients were refractory to lenalidomide. Grade 3 or 4 adverse events (AEs) included anemia (17%), decreased lymphocyte count (15%), and hypophosphatemia (10%). Most common serious AEs included sepsis (9.8%) and pneumonia (7.8%). This Phase I trial demonstrates that a JAK inhibitor, ruxolitinib, can overcome refractoriness to lenalidomide and steroids for patients with RRMM. These results represent a promising novel therapeutic approach for treating MM. NCT03110822.

Leveraging electronic patient diaries in SUDEP risk evaluation.

OBJECTIVE: Our aim was to describe the risk factors known to be related to sudden unexpected death in epilepsy (SUDEP) that can be extracted from patients that utilizes an online seizure diary tool (SeizureTracker™). METHOD: We conducted a descriptive analysis of SeizureTracker™ users across factors relevant to SUDEP risk. We also compared our app-using cohort to published SUDEP case-control studies. RESULTS: We report across seven risk factors from 30,813 users of SeizureTracker™ who had a median length of time using the app of 5.69 years (range from 1 month to 15 years). We found that they are at greater risk for SUDEP than groups from published studies (p < .00001) based on the risk factor of generalized tonic-clonic seizures. SIGNIFICANCE: We demonstrated that the population using the SeizureTracker™ tool can be a valuable population for expanding investigation of SUDEP risk factors and is a first step towards establishing a large sample with a method to ascertain data prospectively that might be critical to developing a SUDEP risk algorithm.

Bluetooth-Enabled Implantable Cardiac Monitors and Two-Way Smartphone Communication for Patients With Hypertrophic Cardiomyopathy.

Background: Sudden cardiac death (SCD) risk stratification in hypertrophic cardiomyopathy (HCM) currently relies on arrhythmic burden quantification by 24 or 48-hour Holter monitoring. Whether this approach adequately captures arrhythmic burden, compared with longer-term continuous monitoring, is unclear. We sought to assess the long-term incidence of nonsustained ventricular tachycardia (NSVT) in HCM patients at low or moderate SCD risk, using implantable cardiac monitors (ICMs) paired with a novel Bluetooth-enabled 2-way communication platform. Methods: This prospective, single-arm, observational study enrolled 33 HCM patients. Patients were implanted with an Abbott (Chicago, IL) Confirm Rx ICM and monitored using a protocolized care pathway. Results: A total of 20 patients (60.6%) had ≥ 1 episode of NSVT recorded on the ICM, the majority of whom had previous Holter monitors that did not identify NSVT (60%, n = 12). A total of 71 episodes of NSVT were detected. Median time to first NSVT detection was 76.5 days (range: 0-553 days). A total of 19 patients underwent primary prevention implantable cardioverter defibrillator implantation during an average follow-up of 544 days (range: 42-925 days). A total of 172,112 automatic transmissions were received, and 65 (0.04%) required clinical follow-up. A total of 325 manual transmissions were received and managed. A total of 14 manual transmissions (4.3%) required follow-up, whereas 311 (95.7%) were managed solely with a text message. Conclusions: Surveillance and reporting systems utilizing 2-way communication enabled by novel ICMs are feasible and allow remote management of patients with HCM. Prolonged monitoring with ICMs identified more patients with nonsustained arrythmias than did standard Holter monitoring. In many cases, this information impacted both SCD risk stratification and patient management.

Contexte: La stratification du risque de mort cardiaque subite (MCS) dans la cardiomyopathie hypertrophique (CMH) dépend actuellement de la quantification de la charge arythmique par une surveillance Holter de 24 ou 48 heures. Il n'est pas clair si cette approche permet d'évaluer adéquatement la charge arythmique, comparativement à une surveillance continue à plus long terme. Nous avons cherché à évaluer la fréquence à long terme de la tachycardie ventriculaire non soutenue (TVNS) chez des patients atteints de CMH à risque faible ou modéré de MCS, au moyen de moniteurs cardiaques implantables (MCI) couplés à une nouvelle plate-forme de communication bidirectionnelle utilisable avec Bluetooth. Méthodologie: Cette étude par observation prospective comportant un seul groupe a été menée auprès de 33 patients atteints de CMH. Les patients ont reçu un MCI Confirm Rx d'Abbott (Chicago, États-Unis) et ont été surveillés dans le cadre d'un parcours de soins reposant sur un protocole. Résultats: Au total, 20 patients (60,6 %) ont eu au moins un épisode de TVNS enregistré par le MCI. La majorité de ces patients portaient déjà un moniteur Holter qui n'a pas décelé de TVNS (60 %, n = 12). Au total, 71 épisodes de TVNS ont été détectés. Le temps médian écoulé avant la première détection de TVNS était de 76,5 jours (fourchette : 0-553 jours). Au total, 19 patients se sont fait poser un défibrillateur cardioverteur implantable en prévention primaire pendant un suivi moyen de 544 jours (fourchette : 42-925 jours). En tout, 172 112 transmissions automatiques ont été reçues, et 65 (0,04 %) ont nécessité un suivi clinique. Par ailleurs, 325 transmissions manuelles ont été reçues et traitées. De ce nombre, 14 transmissions (4,3 %) ont nécessité un suivi, tandis que 311 (95,7 %) ont été traitées uniquement au moyen d'un message texte. Conclusions: Les systèmes de surveillance et de signalement utilisant une communication bidirectionnelle rendue possible grâce aux nouveaux MCI sont réalisables et permettent une prise en charge à distance des patients atteints d'un CMH. La surveillance prolongée par un MCI a permis de déceler plus d'arythmies non soutenues que la surveillance Holter type. Dans de nombreux cas, ces renseignements ont eu un effet positif tant sur la stratification du risque de MCS que sur la prise en charge des patients.

Computational, Mechanistic, and Experimental Insights into Regioselective Catalytic C-C Bond Activation in Linear 1-Aza-[3]triphenylene.

C-C bond activation by transition metal complexes in ring-strained compounds followed by annulation with unsaturated compounds is an efficient approach to generate structurally more complex compounds. However, the site of catalytic C-C bond activation is difficult to predict in unsymmetrically substituted polycyclic systems. Here, we report a study on the (regio)selective catalytic cleavage of selected C-C bonds in 1-aza-[3]triphenylene, followed by annulation with alkynes, forming products with extended π-conjugated frameworks. Based on density functional theory (DFT) calculations, we established the stability of possible transition metal intermediates formed by oxidative addition to the C-C bond and thus identified the likely site of C-C bond activation. The computationally predicted selectivity was confirmed by the following experimental tests for the corresponding Ir-catalyzed C-C cleavage reaction followed by an alkyne insertion that yielded mixtures of two mono-insertion products isolated with yields of 34-36%, due to the close reactivity of two bonds during the first C-C bond activation. Similar results were obtained for twofold Ir- or Rh-catalyzed insertion reactions, with higher yields of 72-77%. In a broader context, by combining DFT calculations, which provided insights into the relative reactivity of individual C-C bonds, with experimental results, our approach allows us to synthesize previously unknown pentacyclic azaaromatic compounds.

Transcatheter Tricuspid Valve Replacement With the EVOQUE System: 1-Year Outcomes of a Multicenter, First-in-Human Experience.

OBJECTIVES: The aim of this study was to report the midterm outcomes at 1 year in the expanded first-in-human experience with the transfemoral EVOQUE system (Edwards Lifesciences) for tricuspid regurgitation (TR). BACKGROUND: Untreated TR is associated with excess mortality and morbidity. The first-in-human experience with the EVOQUE tricuspid valve replacement system reported favorable 30-day outcomes with no mortality in a compassionate use population. METHODS: Twenty-seven patients with severe TR were treated with the EVOQUE system in a compassionate use experience at 7 centers between May 2019 and July 2020. All patients had clinical right-sided heart failure (HF) and were deemed inoperable and unsuitable for transcatheter edge-to-edge repair by the institutional heart teams. The clinical outcomes collected included all-cause mortality, symptom status, TR severity, HF hospitalization, and major adverse cardiovascular events. RESULTS: At baseline, all patients (age: 77 ± 8 years, 89% female) were at high surgical risk (mean Society of Thoracic Surgeons score: 8.6% ± 5.5%), with 89% New York Heart Association functional class III/IV. TR was predominantly functional in etiology (19/27, 70%). At 1 year, mortality was 7% (2/27), 70% of patients were New York Heart Association functional class I/II, and 96% and 87% of patients had a TR grade ≤2+ and ≤1+, respectively. Between 30 days and 1 year, 2 patients experienced HF hospitalizations, and 1 patient required a new pacemaker implantation. CONCLUSIONS: In this early, compassionate use experience, the transfemoral transcatheter EVOQUE tricuspid valve replacement system demonstrated durable efficacy, persistent improvement in symptom status, and low rates of mortality and HF hospitalizations at a 1-year follow-up. Further studies are underway to validate its efficacy.