Persistence on therapy and propensity matched outcome comparison of two subcutaneous interferon beta 1a dosages for multiple sclerosis

To compare treatment persistence between two dosages of interferon β-1a in a large observational multiple sclerosis registry and assess disease outcomes of first line MS treatment at these dosages using propensity scoring to adjust for baseline imbalance in disease characteristics. Treatment discontinuations were evaluated in all patients within the MSBase registry who commenced interferon β-1a SC thrice weekly (n = 4678). Furthermore, we assessed 2-year clinical outcomes in 1220 patients treated with interferon β-1a in either dosage (22 µg or 44 µg) as their first disease modifying agent, matched on propensity score calculated from pre-treatment demographic and clinical variables. A subgroup analysis was performed on 456 matched patients who also had baseline MRI variables recorded. Overall, 4054 treatment discontinuations were recorded in 3059 patients. The patients receiving the lower interferon dosage were more likely to discontinue treatment than those with the higher dosage (25% vs. 20% annual probability of discontinuation, respectively). This was seen in discontinuations with reasons recorded as “lack of efficacy” (3.3% vs. 1.7%), “scheduled stop” (2.2% vs. 1.3%) or without the reason recorded (16.7% vs. 13.3% annual discontinuation rate, 22 µg vs. 44 µg dosage, respectively). Propensity score was determined by treating centre and disability (score without MRI parameters) or centre, sex and number of contrast-enhancing lesions (score including MRI parameters). No differences in clinical outcomes at two years (relapse rate, time relapse-free and disability) were observed between the matched patients treated with either of the interferon dosages. Treatment discontinuations were more common in interferon β-1a 22 µg SC thrice weekly. However, 2-year clinical outcomes did not differ between patients receiving the different dosages, thus replicating in a registry dataset derived from “real-world” database the results of the pivotal randomised trial. Propensity score matching effectively minimised baseline covariate imbalance between two directly compared sub-populations from a large observational registry

Apprentissage profond de phylogénies pour révéler la dynamique des flambées épidémiques

International audienceWidely applicable, accurate and fast inference methods in phylodynamics are needed to fully profit from the richness of genetic data in uncovering the dynamics of epidemics. Standard methods, including maximum-likelihood and Bayesian approaches, generally rely on complex mathematical formulae and approximations, and do not scale with dataset size. We develop a likelihood-free, simulation-based approach, which combines deep learning with (1) a large set of summary statistics measured on phylogenies or (2) a complete and compact representation of trees, which avoids potential limitations of summary statistics and applies to any phylodynamics model. Our method enables both model selection and estimation of epidemiological parameters from very large phylogenies. We demonstrate its speed and accuracy on simulated data, where it performs better than the state-of-the-art methods. To illustrate its applicability, we assess the dynamics induced by superspreading individuals in an HIV dataset of men-having-sex-with-men in Zurich. Our tool PhyloDeep is available on github.com/evolbioinfo/phylodeep

Deep learning from phylogenies to uncover the transmission dynamics of epidemics

Widely applicable, accurate and fast inference methods in phylodynamics are needed to fully profit from the richness of genetic data in uncovering the dynamics of epidemics. Standard methods, including maximum-likelihood and Bayesian approaches, which are both model specific, often rely on complex mathematical formulae and approximations, and do not scale well with dataset size. We develop a likelihood-free, simulation-based approach, which combines deep learning with (1) a large set of summary statistics measured on phylogenies or (2) a complete and compact vectorial representation of trees, which avoids potential limitations of summary statistics and applies to any phylodynamic model. Our method enables both model selection and estimation of epidemiological parameters. We demonstrate its speed and accuracy on simulated data, where it performs better than the state-of-the-art methods. To illustrate its applicability, we assess the dynamics induced by superspreading individuals in an HIV dataset of men- having-sex-with-men in Zurich

Apprentissage profond de phylogénies pour révéler la dynamique des flambées épidémiques

International audienceWidely applicable, accurate and fast inference methods in phylodynamics are needed to fully profit from the richness of genetic data in uncovering the dynamics of epidemics. Standard methods, including maximum-likelihood and Bayesian approaches, generally rely on complex mathematical formulae and approximations, and do not scale with dataset size. We develop a likelihood-free, simulation-based approach, which combines deep learning with (1) a large set of summary statistics measured on phylogenies or (2) a complete and compact representation of trees, which avoids potential limitations of summary statistics and applies to any phylodynamics model. Our method enables both model selection and estimation of epidemiological parameters from very large phylogenies. We demonstrate its speed and accuracy on simulated data, where it performs better than the state-of-the-art methods. To illustrate its applicability, we assess the dynamics induced by superspreading individuals in an HIV dataset of men-having-sex-with-men in Zurich. Our tool PhyloDeep is available on github.com/evolbioinfo/phylodeep

The contrasting phylodynamics of human inuenza B viruses

10.7554/eLife.05055eLife2015

Outbreak investigation for toxigenic Corynebacterium diphtheriae wound infections in refugees from Northeast Africa and Syria in Switzerland and Germany by whole genome sequencing

Toxigenic Corynebacterium diphtheriae is an important and potentially fatal threat to patients and public health. During the current dramatic influx of refugees into Europe, our objective was to use whole genome sequencing for the characterization of a suspected outbreak of C. diphtheriae wound infections among refugees. After conventional culture, we identified C. diphtheriae using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) and investigated toxigenicity by PCR. Whole genome sequencing was performed on a MiSeq Illumina with >70×coverage, 2×250 bp read length, and mapping against a reference genome. Twenty cases of cutaneous C. diphtheriae in refugees from East African countries and Syria identified between April and August 2015 were included. Patients presented with wound infections shortly after arrival in Switzerland and Germany. Toxin production was detected in 9/20 (45%) isolates. Whole genome sequencing-based typing revealed relatedness between isolates using neighbour-joining algorithms. We detected three separate clusters among epidemiologically related refugees. Although the isolates within a cluster showed strong relatedness, isolates differed by >50 nucleotide polymorphisms. Toxigenic C. diphtheriae associated wound infections are currently observed more frequently in Europe, due to refugees travelling under poor hygienic conditions. Close genetic relatedness of C. diphtheriae isolates from 20 refugees with wound infections indicates likely transmission between patients. However, the diversity within each cluster and phylogenetic time-tree analysis suggest that transmissions happened several months ago, most likely outside Europe. Whole genome sequencing offers the potential to describe outbreaks at very high resolution and is a helpful tool in infection tracking and identification of transmission routes

The Kurtzke EDSS rank stability increases 4 years after the onset of multiple sclerosis: results from the MSBase Registry.

Background The Expanded Disability Status Scale (EDSS) is widely used to rate multiple sclerosis (MS) disability, but lack of disease duration information limits utility in assessing severity. EDSS ranking at specific disease durations was used to devise the MS Severity Score, which is gaining popularity for predicting outcomes. As this requires validation in longitudinal cohorts, we aimed to assess the utility of EDSS ranking as a predictor of 5-year outcome in the MSBase Registry. Methods Rank stability of EDSS over time was examined in the MSBase Registry, a large multicentre MS cohort. Scores were ranked for 5-year intervals, and correlation of rank across intervals was assessed using Spearman's rank correlation. EDSS progression outcomes at 10 years were disaggregated by 5-year EDSS score