Association of reductions in rescue medication requirements with vagus nerve stimulation: Results of long-term community collected data from a seizure diary app.

OBJECTIVE: To assess the impact of vagus nerve stimulation (VNS) on quality of life contributors such as rescue medications. METHODS: Using the seizure diary application SeizureTracker™ database, we examined trends in rescue administration frequency before and after the first recorded VNS magnet swipe in patients with drug-resistant epilepsy who had 1) At least one VNS magnet swipe recorded in the diary, and 2) Recorded usage of a benzodiazepine rescue medication (RM) within 90 days prior to the first swipe. A paired Wilcoxon rank-sum test was used to assess changes in RM usage frequency between 30-, 60-, 90-, 180- and 360-day intervals beginning 30 days after first magnet swipe. Longitudinal changes in RM usage frequency were assessed with a generalized estimating equation model. RESULTS: We analyzed data of 95 patients who met the inclusion criteria. Median baseline seizure frequency was 8.3 seizures per month, with median baseline rescue medication usage frequency of 2.1 administrations per month (SD 3.3). Significant reductions in rescue medication usage were observed in the 91 to 180 day interval after first VNS magnet swipe, and at 181 to 360 days and at 361 to 720 days, with the magnitude of reduction increasing over time. Decreases in rescue medication usage were sustained when controlling for patients who did not record rescue medication use after the first VNS magnet swipe (N=91). Significant predictors of reductions in rescue medication included baseline frequency of rescue medication usage and time after first VNS magnet swipe. SIGNIFICANCE: This retrospective analysis suggests that usage of rescue medications is reduced following the start of VNS treatment in patients with epilepsy, and that the magnitude of reduction may progressively increase over time.

EVOQUE Tricuspid Valve Replacement System: State-of-the-Art Screening and Intraprocedural Guidance.

With the recent approval of the transcatheter EVOQUE tricuspid valve replacement system to treat severe, symptomatic tricuspid regurgitation, there is a need to define the appropriate patient population and anatomical considerations for this device. In this consensus document, the authors review these considerations, describe the procedural steps and imaging requirements to ensure technical success, and discuss management of complex intraprocedural circumstances.

Opportunities to strengthen respiratory virus surveillance systems in Australia: lessons learned from the COVID-19 response.

Abstract: Disease surveillance data was critical in supporting public health decisions throughout the coronavirus disease 2019 (COVID-19) pandemic. At the same time, the unprecedented circumstances of the pandemic revealed many shortcomings of surveillance systems for viral respiratory pathogens. Strengthening of surveillance systems was identified as a priority for the recently established Australian Centre for Disease Control, which represents a critical opportunity to review pre-pandemic and pandemic surveillance practices, and to decide on future priorities, during both pandemic and inter-pandemic periods. On 20 October 2022, we ran a workshop with experts from the academic and government sectors who had contributed to the COVID-19 response in Australia on 'The role of surveillance in epidemic response', at the University of New South Wales, Sydney, Australia. Following the workshop, we developed five recommendations to strengthen respiratory virus surveillance systems in Australia, which we present here. Our recommendations are not intended to be exhaustive. We instead chose to focus on data types that are highly valuable yet typically overlooked by surveillance planners. Three of the recommendations focus on data collection activities that support the monitoring and prediction of disease impact and the effectiveness of interventions (what to measure) and two focus on surveillance methods and capabilities (how to measure). Implementation of our recommendations would enable more robust, timely, and impactful epidemic analysis.

Necessary for seizure forecasting outcome metrics: seizure frequency and benchmark model.

Work is ongoing to advance seizure forecasting, but the performance metrics used to evaluate model effectiveness can sometimes lead to misleading outcomes. For example, some metrics improve when tested on patients with a particular range of seizure frequencies (SF). This study illustrates the connection between SF and metrics. Additionally, we compared benchmarks for testing performance: a moving average (MA) or the commonly used permutation benchmark. Three data sets were used for the evaluations: (1) Self-reported seizure diaries of 3,994 Seizure Tracker patients; (2) Automatically detected (and sometimes manually reported or edited) generalized tonic-clonic seizures from 2,350 Empatica Embrace 2 and Mate App seizure diary users, and (3) Simulated datasets with varying SFs. Metrics of calibration and discrimination were computed for each dataset, comparing MA and permutation performance across SF values. Most metrics were found to depend on SF. The MA model outperformed or matched the permutation model in all cases. The findings highlight SF's role in seizure forecasting accuracy and the MA model's suitability as a benchmark. This underscores the need for considering patient SF in forecasting studies and suggests the MA model may provide a better standard for evaluating future seizure forecasting models.

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.

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.

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.

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.