12-Month Outcomes of the US Patient Cohort in the SONATA Pivotal IDE Trial of Transcervical Ablation of Uterine Fibroids.

Objective: The prospective SONATA pivotal Investigational Device Exemption (IDE) trial was performed in the United States (US) and Mexico to examine the safety and effectiveness of transcervical fibroid ablation (TFA) in the treatment of symptomatic uterine fibroids. This is an analysis of 12-month clinical outcomes in the US cohort. Methods: TFA with the Sonata System was performed on women with symptomatic uterine fibroids. The 12-month co-primary endpoints were reduction in menstrual blood loss and freedom from surgical reintervention. Symptom severity, quality of life, patient satisfaction, safety, and reductions in uterine and fibroid volumes were also evaluated. Results: One hundred twenty-five patients were enrolled and treated in the US. Both co-primary endpoints were achieved in this US-based cohort, as 65.3% of patients reported ≥50% reduction in menstrual bleeding and 99.2% of patients were free from surgical reintervention. Symptom improvement was noted by 97.4% of patients and 98.3% were satisfied. Ninety-five percent of patients reported reduced menstrual bleeding at 12 months, and 86.8% noted \u3e20% reduction. Significant mean improvements at 12 months were realized in both symptom severity and health-related quality of life (33.8 points and 45.8 points, respectively; all P\u3c0.0001). Mean maximal fibroid volume reduction per patient was 63.8%. There was a 0% incidence of device related adverse events. Mean length of stay was 2.5 hrs and 50% of patients returned to normal activity within 1 day. Conclusion: This analysis of US patients in the SONATA pivotal IDE trial demonstrates results consistent with those in the full cohort. TFA with Sonata significantly reduced fibroid symptoms with a low surgical reintervention rate through 12 months. These results support the efficacy and safety of the Sonata system as a first-line treatment for women affected by symptomatic uterine fibroids

Timing and predictors of severe rotavirus gastroenteritis among unvaccinated infants in low- and middle-income countries

Delays in rotavirus vaccine schedule could improve performance in low- and middle-income countries (LMICs). However, delaying the first dose could be detrimental if infants experience severe rotavirus gastroenteritis (RVGE) early in life. Our objective was to describe the timing and predictors of severe RVGE in unvaccinated children in LMICs. We analysed the placebo arms from two clinical trials (cohort 1: NCT00241644; cohort 2: NCT00362648). We estimated the rate, cumulative incidence (per 1000 infants) and age distribution of severe RVGE episodes. Cox proportional hazards models were used to estimate hazard ratios and 95% confidence intervals (CI) for the association between baseline factors and severe RVGE. Cumulative incidence at 6 months of age was 23/1000 (95% CI 15-30) in cohort 1 and 6/1000 (95% CI 3-8) in cohort 2. Early antibiotic use (compared with no use) was associated with 2.03 (95% CI 1.18-3.48) and 1.41 (95% CI 0.80-2.51) times the rate of severe RVGE in cohorts 1 and 2, respectively. The cumulative incidence of severe RVGE was low at 6 months of age, suggesting that a 4-week delay in the vaccination schedule may not result in a large number of severe RVGE episodes prior to vaccine receipt. Copyright © Cambridge University Press 2018

An opportunity for primary prevention research in psychotic disorders

An opportunity has opened for research into primary prevention of psychotic disorders, based on progress in endophenotypes, genetics, and genomics. Primary prevention requires reliable prediction of susceptibility before any symptoms are present. We studied a battery of measures where published data supports abnormalities of these measurements prior to appearance of initial psychosis symptoms. These neurobiological and behavioral measurements included cognition, eye movement tracking, Event Related Potentials, and polygenic risk scores. They generated an acceptably precise separation of healthy controls from outpatients with a psychotic disorder. Methods The Bipolar and Schizophrenia Network on Intermediate Phenotypes (B-SNIP) measured this battery in an ancestry-diverse series of consecutively recruited adult outpatients with a psychotic disorder and healthy controls. Participants include all genders, 16 to 50 years of age, 261 with psychotic disorders (Schizophrenia (SZ) 109, Bipolar with psychosis (BPP) 92, Schizoaffective disorder (SAD) 60), 110 healthy controls. Logistic Regression, and an extension of the Linear Mixed Model to include analysis of pairwise interactions between measures (Environmental kernel Relationship Matrices (ERM)) with multiple iterations, were performed to predict case-control status. Each regression analysis was validated with four-fold cross-validation. Results and conclusions Sensitivity, specificity, and Area Under the Curve of Receiver Operating Characteristic of 85%, 62%, and 86%, respectively, were obtained for both analytic methods. These prediction metrics demonstrate a promising diagnostic distinction based on premorbid risk variables. There were also statistically significant pairwise interactions between measures in the ERM model. The strong prediction metrics of both types of analytic model provide proof-of-principle for biologically-based laboratory tests as a first step toward primary prevention studies. Prospective studies of adolescents at elevated risk, vs. healthy adolescent controls, would be a next step toward development of primary prevention strategies

A flexible, computationally efficient method for fitting the proportional hazards model to interval-censored data: A Novel Method for Fitting the Proportional Hazards Model to Interval-Censored Data

The proportional hazards model (PH) is currently the most popular regression model for analyzing time-to-event data. Despite its popularity, the analysis of interval-censored data under the PH model can be challenging using many available techniques. This paper presents a new method for analyzing interval-censored data under the PH model. The proposed approach uses a monotone spline representation to approximate the unknown nondecreasing cumulative baseline hazard function. Formulating the PH model in this fashion results in a finite number of parameters to estimate while maintaining substantial modeling flexibility. A novel expectation-maximization (EM) algorithm is developed for finding the maximum likelihood estimates of the parameters. The derivation of the EM algorithm relies on a two-stage data augmentation involving latent Poisson random variables. The resulting algorithm is easy to implement, robust to initialization, enjoys quick convergence, and provides closed-form variance estimates. The performance of the proposed regression methodology is evaluated through a simulation study, and is further illustrated using data from a large population-based randomized trial designed and sponsored by the United States National Cancer Institute

Unique Molecular Identifiers and Multiplexing Amplicons Maximize the Utility of Deep Sequencing To Critically Assess Population Diversity in RNA Viruses

Next generation sequencing (NGS)/deep sequencing has become an important tool in the study of viruses. The use of unique molecular identifiers (UMI) can overcome the limitations of PCR errors and PCR-mediated recombination and reveal the true sampling depth of a viral population being sequenced in an NGS experiment. This approach of enhanced sequence data represents an ideal tool to study both high and low abundance drug resistance mutations and more generally to explore the genetic structure of viral populations. Central to the use of the UMI/Primer ID approach is the creation of a template consensus sequence (TCS) for each genome sequenced. Here we describe a series of experiments to validate several aspects of the Multiplexed Primer ID (MPID) sequencing approach using the MiSeq platform. We have evaluated how multiplexing of cDNA synthesis and amplicons affects the sampling depth of the viral population for each individual cDNA and amplicon to understand the relationship between broader genome coverage versus maximal sequencing depth. We have validated reproducibility of the MPID assay in the detection of minority mutations in viral genomes. We have also examined the determinants that allow sequencing reads of PCR recombinants to contaminate the final TCS data set and show how such contamination can be limited. Finally, we provide several examples where we have applied MPID to analyze features of minority variants and describe limits on their detection in viral populations of HIV-1 and SARS-CoV-2 to demonstrate the generalizable utility of this approach with any RNA virus