Citations

Introduction: CRT optimizations has the potential to improve the 30% non-response rate. Randomized trials (RCTs) comparing device and echo-guided CRT optimization have been overall small and results have been conflicting. Methods: We searched the EMBASE and MEDLINE databases from January 2008 through September 2020 for RCTs and prospective observational studies that randomized patients to either echo-guided or device-based CRT optimization. Included studies had to report on at least one of the following outcomes of interest: all-cause mortality, heart failure hospitalization (HFH), NYHA class improvement, mean EF at follow up, LVEDD, 6MWD, and QoL. A meta-analyses on the outcomes of interest was performed using random effects models. Results: We included seven RCTs representing a total of 2,730 patients. Of those, 1,113 patients were randomized to echo-guided optimization, and 1,617 were randomized to device-based optimization. The mean age of patients was 64.5 years, and they were predominantly men (60%). In the device-based optimization group, there was 93 deaths (5.7%) and 142 HFH (5.1%). In the echo-guided optimization group, there was 57 deaths (10.2%) and 112 HFH (12.4%). On random effects meta-analysis, the device-based optimization group had a lower rate of HFH compared with the echo-guided optimization group (OR 0.81, 95% CL 0.70-0.95, Figure). However, differences in the following outcomes between the two groups were not statistically significant: all-cause mortality, mean follow up EF, LVEDD, NYHA class improvement, 6MWD improvement range or QoL. Conclusions: This meta-analysis suggests that echo-guided optimization of CRT is equivalent to device-based optimization in relation to the clinical outcomes of death, EF, LVEDD, NYHA and 6MWD but suggests that device-related optimization could reduce HFH. The latter merits further investigation in relation to different device-base optimization algorithms.

Abstract This study describes a generalized cross‐patient seizure‐forecasting approach using recurrent neural networks with ultra‐long‐term subcutaneous EEG (sqEEG) recordings. Data from six patients diagnosed with refractory epilepsy and monitored with an sqEEG device were used to develop a generalized algorithm for seizure forecasting using long short‐term memory (LSTM) deep‐learning classifiers. Electrographic seizures were identified by a board‐certified epileptologist. One‐minute data segments were labeled as preictal or interictal based on their relationship to confirmed seizures. Data were separated into training and testing data sets, and to compensate for the unbalanced data ratio in training, noise‐added copies of preictal data segments were generated to expand the training data set. The mean and standard deviation (SD) of the training data were used to normalize all data, preserving the pseudo‐prospective nature of the analysis. Different architecture classifiers were trained and tested using a leave‐one‐patient‐out cross‐validation method, and the area under the receiver‐operating characteristic (ROC) curve (AUC) was used to evaluate the performance classifiers. The importance of each input signal was evaluated using a leave‐one‐signal‐out method with repeated training and testing for each classifier. Cross‐patient classifiers achieved performance significantly better than chance in four of the six patients and an overall mean AUC of 0.602 ± 0.126 (mean ± SD). A time in warning of 37.386% ± 5.006% (mean ± std) and sensitivity of 0.691 ± 0.068 (mean ± std) were observed for patients with better than chance results. Analysis of input channels showed a significant contribution ( p < .05) by the Fourier transform of signals channels to overall classifier performance. The relative contribution of input signals varied among patients and architectures, suggesting that the inclusion of all signals contributes to robustness in a cross‐patient classifier. These early results show that it is possible to forecast seizures training with data from different patients using two‐channel ultra‐long‐term sqEEG.

Abstract Herein, we present the long-term follow-up of the randomized E1912 trial comparing the long-term efficacy of ibrutinib–rituximab (IR) therapy to fludarabine, cyclophosphamide, and rituximab (FCR) and describe the tolerability of continuous ibrutinib. The E1912 trial enrolled 529 treatment-naïve patients aged ≤70 years with chronic lymphocytic leukemia (CLL). Patients were randomly assigned (2:1 ratio) to receive IR or 6 cycles of FCR. With a median follow-up of 5.8 years, median progression-free survival (PFS) is superior for IR (hazard ratio [HR], 0.37; P &lt; .001). IR improved PFS relative to FCR in patients with both immunoglobulin heavy chain variable region (IGHV) gene mutated CLL (HR: 0.27; P &lt; .001) and IGHV unmutated CLL (HR: 0.27; P &lt; .001). Among the 354 patients randomized to IR, 214 (60.5%) currently remain on ibrutinib. Among the 138 IR-treated patients who discontinued treatment, 37 (10.5% of patients who started IR) discontinued therapy due to disease progression or death, 77 (21.9% of patients who started IR) discontinued therapy for adverse events (AEs)/complications, and 24 (6.8% of patients who started IR) withdrew for other reasons. Progression was uncommon among patients able to remain on ibrutinib. The median time from ibrutinib discontinuation to disease progression or death among those who discontinued treatment for a reason other than progression was 25 months. Sustained improvement in overall survival (OS) was observed for patients in the IR arm (HR, 0.47; P = .018). In conclusion, IR therapy offers superior PFS relative to FCR in patients with IGHV mutated or unmutated CLL, as well as superior OS. Continuous ibrutinib therapy is tolerated beyond 5 years in the majority of CLL patients. This trial was registered at www.clinicaltrials.gov as #NCT02048813.

Abstract Objective One of the most disabling aspects of living with chronic epilepsy is the unpredictability of seizures. Cumulative research in the past decades has advanced our understanding of the dynamics of seizure risk. Technological advances have recently made it possible to record pertinent biological signals, including electroencephalogram (EEG), continuously. We aimed to assess whether patient‐specific seizure forecasting is possible using remote, minimally invasive ultra‐long‐term subcutaneous EEG. Methods We analyzed a two‐center cohort of ultra‐long‐term subcutaneous EEG recordings, including six patients with drug‐resistant focal epilepsy monitored for 46–230 days with median 18 h/day of recorded data, totaling >11 000 h of EEG. Total electrographic seizures identified by visual review ranged from 12 to 36 per patient. Three candidate subject‐specific long short‐term memory network deep learning classifiers were trained offline and pseudoprospectively on preictal (1 h before) and interictal (>1 day from seizures) EEG segments. Performance was assessed relative to a random predictor. Periodicity of the final forecasts was also investigated with autocorrelation. Results Depending on each architecture, significant forecasting performance was achieved in three to five of six patients, with overall mean area under the receiver operating characteristic curve of .65–.74. Significant forecasts showed sensitivity ranging from 64% to 80% and time in warning from 10.9% to 44.4%. Overall, the output of the forecasts closely followed patient‐specific circadian patterns of seizure occurrence. Significance This study demonstrates proof‐of‐principle for the possibility of subject‐specific seizure forecasting using a minimally invasive subcutaneous EEG device capable of ultra‐long‐term at‐home recordings. These results are encouraging for the development of a prospective seizure forecasting trial with minimally invasive EEG.

Background: COVID-19 reveals new manifestations not only in the acute phase but also in the long term. The latter is what has been termed as ‘post acute sequelae of SARS-CoV-2 infection’ (PASC) — or “long COVID”— with dyspnea as one of its hallmarks. Cardiopulmonary stress testing (CPET) is increasingly being used to better evaluate such patients. Objective: To compare CPET findings of long COVID patients versus non-COVID patients with undifferentiated dyspnea on exertion seen at Mayo Clinic. Methods: A cohort study was carried out using Mayo Clinic’s exercise testing database. The study group included 77 PASC patients confirmed by a positive antigen or PCR test with no prior history of heart or lung disease. PASC patients were compared to 766 non-COVID patients with unspecified dyspnea without known diagnosis of cardiac or pulmonary disease. ANOVA according to the general linear model and Pearson’s Chi square were used for the bivariate analysis of the quantitative and categorical variables, respectively. Analyses were controlled for age, sex, and beta blocker use as appropriate. Both HR and SBP at rest and peak exercise were significantly different between groups, although the absolute differences were small. Results: Compared with the control group, the post-COVID patients were younger (mean age 47 SD ±14.9 years versus 50.5 SD ±10.1 years) and more likely to be female (70.1% (54 of 77) vs 57.6% (441 of 766)). Although there were many significant differences in the CPET results, the most outstanding were a lower FAC (73 SD ±19.4% vs 85.2 SD ±20.8%) and peak VO2 (22 SD ±6.4 ml/kg/min vs 25.8 SD ±7.37 ml/kg/min) in the study group (p<.0001). Also, CNS changes during CPET were reported at higher rates (19.5% (15 of 77) vs 11.4% (87 of 766)) while dyspnea at lower rates (55.8% (43 of 77) vs 65.7% (503 of 766)) in post-COVID patients (p=0.02). When adjusting for age and sex, weight was significantly higher in the study group (p=0.03) although the absolute difference was modest and BMI remained not significant. PFTs did not suggest any pulmonary abnormalities. Conclusions: We identified a high rate of severe exercise limitation among long COVID patients. Young women may be at higher risk for these complications. Pre-exercise PFTs and absence of objective respiratory limitation during CPET argue against a primary pulmonary cause of the observed limitations, though evaluation of cardiac, autonomic, and muscular impairment is pending. Further studies are warranted to untangle the physiologic abnormalities responsible for these observations.

AbstractObjectiveEsthesioneuroblastoma (ENB) is a rare malignant neoplasm arising from the olfactory epithelium of the cribriform plate. The goal of this study was to update our oncologic outcomes for this disease and explore prognostic factors associated with survival.Materials and methodsWe performed a retrospective analysis of patients with ENB treated at a single tertiary care institution from January 1, 1960, to January 1, 2020. Univariate and multivariate analysis was performed. Overall survival (OS), progression‐free survival (PFS), and distant metastasis–free survival (DMFS) were reported.ResultsAmong 143 included patients, the 5‐year OS was 82.3% and the 5‐year PFS was 51.6%; 5‐year OS and PFS have improved in the modern era (2005–present). Delayed regional nodal metastasis was the most common site of recurrence in 22% of patients (median, 57 months). On univariate analysis, modified Kadish staging (mKadish) had a negative effect on OS, PFS, and DMFS (p < 0.05). Higher Hyams grade had a negative effect on PFS and DMFS (p < 0.05). Positive margin status had a negative effect on PFS (p < 0.05). Orbital invasion demonstrated worsening OS (hazard ratio, 3.1; p < 0.05). On multivariable analysis, high Hyams grade (3 or 4), high mKadish stage (C+D), and increasing age were independent negative prognostic factors for OS (p < 0.05). High Hyams grade (3+4), high mKadish stage (C+D), age, and positive margin status were independent negative prognostic factors for PFS (p < 0.05). High Hyams grade (3+4) was an independent negative prognostic factor for DMFS (p < 0.05).ConclusionsPatients with low Hyams grade and mKadish stage have favorable 5‐year OS, PFS, and DMFS.

Introduction Rheumatoid arthritis (RA) affects approximately 0.5%–1% of the general population. Clinically significant interstitial lung diseases (ILD) develops in just under 10% of people with RA, and subclinical disease is more common. Little is known about RA-ILD in New Zealand (NZ), or the number of persons with RA in Canterbury, NZ. This study aims to determine: (1) incidence and prevalence of RA, (2) incidence and prevalence of RA-ILD, (3) clinical characteristics and risk factors for the development of RA-ILD, (4) long-term outcomes of RA-ILD, in the population resident within the Canterbury District Health Board (CDHB) catchment area. Methods and analysis Persons aged 18 years of age and older, and resident in the region covered by the CDHB with RA as well as RA-ILD will be identified by retrospective review of medical records. Prevalent as well as incident cases of RA between 1 January 2006 and 31 December 2008 and between 1 January 2011 and 31 December 2013 will be identified, and followed until 30 June 2019. Existing as well as incident cases of RA-ILD during this time will be identified. The association between the development of ILD and clinical characteristics and environmental exposures will be examined using Cox-proportional hazard models. Kaplan-Meier methods will be used to estimate survival rates for patients with RA-ILD. Mortality for people with RA and RA-ILD will also be compared with the general population of the CDHB. Ethics and dissemination Data will be obtained by retrospective review of medical records. Deidentified patient data will be stored in a secure online database. Data on individual patients will not be released, and all results will only be published in aggregate. Ethical approval has been obtained from the University of Otago Human Research Ethics Committee (REF HD18/079). Results will be published in peer-reviewed medical journals and presented at conferences. Trial registration number ACTRN12619001310156; Pre-results.