Natural Cycles app: contraceptive outcomes and demographic analysis of UK users.

PURPOSE: Digital fertility awareness-based contraception offers an alternative choice for women who do not wish to use hormonal or invasive methods. The aim of this study was to investigate the key demographics of current users of the Natural Cycles app and assess the contraceptive outcomes of women preventing pregnancy in a UK cohort of women. MATERIALS AND METHODS: This was a real world observational prospective observational study. The typical-use effectiveness of the method was calculated using both 13-cycle cumulative probability of pregnancy (life table analysis) and Pearl Index for the entire study cohort. Perfect-use PI was calculated using data from cycles where sexual intercourse during the fertile window was marked as protected and no unprotected sex was recorded on fertile days. RESULTS: 12,247 women were included in the study and contributed an average of 9.9 months of data for a total of 10,066 woman years of exposure. The mean age of the cohort was 30, mean BMI 23.4, the majority were in a stable relationship (83.2%) and had a university degree or higher (83%). The one year typical use, PI was 6.1 (95% CI: 5.6, 6.6) and with perfect-use was 2.0 (95% CI: 1.3, 2.8). 13 cycle pregnancy probability was 7.1%. CONCLUSIONS: This is the first study which describes the use of a digital contraceptive by women in the UK. It describes the demographics of users and how they correlate with the apps effectiveness at preventing pregnancy.

Adverse events in second- and third-line treatments for acute and chronic graft-versus-host disease: systematic review.

BACKGROUND: Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is associated with an increased risk of graft-versus-host disease (GvHD), a strong prognostic predictor of early mortality within the first 2 years following allo-HSCT. The objective of this study was to describe the harm outcomes reported among patients receiving second- and third-line treatment as part of the management for GvHD via a systematic literature review. METHODS: A total of 34 studies met the systematic review inclusion criteria, reporting adverse events (AEs) across 12 different second- and third-line therapies. RESULTS: A total of 14 studies reported AEs across nine different therapies used in the treatment of acute GvHD (aGvHD), 17 studies reported AEs of eight different treatments for chronic GvHD (cGvHD) and 3 reported a mixed population. Infections were the AE reported most widely, followed by haematologic events and laboratory abnormalities. Reported infections per patient were lower under extracorporeal photopheresis (ECP) for aGvHD (0.267 infections per patient over 6 months) relative to any of the therapies studied (ranging from 0.853 infections per patient per 6 months under etanercept up to 1.998 infections per patient on inolimomab). CONCLUSION: The reported incidence of infectious AEs in aGvHD and grade 3-5 AEs in cGvHD was lower on ECP compared with pharmaceutical management.

Towards development of a statistical framework to evaluate myotonic dystrophy type 1 mRNA biomarkers in the context of a clinical trial.

Myotonic dystrophy type 1 (DM1) is a rare genetic disorder, characterised by muscular dystrophy, myotonia, and other symptoms. DM1 is caused by the expansion of a CTG repeat in the 3'-untranslated region of DMPK. Longer CTG expansions are associated with greater symptom severity and earlier age at onset. The primary mechanism of pathogenesis is thought to be mediated by a gain of function of the CUG-containing RNA, that leads to trans-dysregulation of RNA metabolism of many other genes. Specifically, the alternative splicing (AS) and alternative polyadenylation (APA) of many genes is known to be disrupted. In the context of clinical trials of emerging DM1 treatments, it is important to be able to objectively quantify treatment efficacy at the level of molecular biomarkers. We show how previously described candidate mRNA biomarkers can be used to model an effective reduction in CTG length, using modern high-dimensional statistics (machine learning), and a blood and muscle mRNA microarray dataset. We show how this model could be used to detect treatment effects in the context of a clinical trial.