Factors Associated With Change in S3-NIV Score Over Time in People With Chronic Respiratory Failure Treated With Long-Term Home Noninvasive Ventilation.

INTRODUCTION: Monitoring changes in symptoms over time during long-term nocturnal home non-invasive ventilation (NIV) using patient-reported outcome measures is crucial. This study aimed to identify factors associated with changes in the S3-NIV total score, its two domains ("respiratory symptoms" and "sleep and NIV-related side effects") and individual item responses. METHODS: We conducted a retrospective, longitudinal data analysis of a cohort of adults with chronic respiratory failure treated with NIV. Data were obtained from a French homecare provider. Multivariate linear and multinomial ordinal mixed effect models were used to identify factors associated with changes in S3-NIV scores over time. RESULTS: Median follow-up was 2 years for 2135 participants. Each participant completed a median of five S3-NIV questionnaires; totaling 11,359 analyzed questionnaires. Type of respiratory condition, sex, age and time since NIV initiation were associated with change in S3-NIV score over time. NIV adherence was not associated with total S3-NIV score but high adherence was associated with more severe respiratory symptoms and an improvement in sleep and NIV-related side effects during the follow-up. Intensity of pressure support was associated with a lower total S3-NIV score and more side effects. Face masks and supplemental oxygen were associated with a lower S3-NIV total score. CONCLUSION: Changes in S3-NIV scores over time are associated with the individual's characteristics and NIV settings. Analysis of the two domains and individual items of the S3-NIV could increase understanding of the difficulties experienced by people on NIV.

The evaluation of Bcftools mpileup and GATK HaplotypeCaller for variant calling in non-human species.

Identification of genetic variations is a central part of population and quantitative genomics studies based on high-throughput sequencing data. Even though popular variant callers such as Bcftools mpileup and GATK HaplotypeCaller were developed nearly 10 years ago, their performance is still largely unknown for non-human species. Here, we showed by benchmark analyses with a simulated insect population that Bcftools mpileup performs better than GATK HaplotypeCaller in terms of recovery rate and accuracy regardless of mapping software. The vast majority of false positives were observed from repeats, especially for GATK HaplotypeCaller. Variant scores calculated by GATK did not clearly distinguish true positives from false positives in the vast majority of cases, implying that hard-filtering with GATK could be challenging. These results suggest that Bcftools mpileup may be the first choice for non-human studies and that variants within repeats might have to be excluded for downstream analyses.