Birth outcomes by type of attendance at antenatal education: An observational study

Background: Antenatal education aims to prepare expectant parents for pregnancy, birth, and parenthood. Studies have reported antenatal education teaching breathing and relaxation methods for pain relief, termed psychoprophylaxis, is associated with reduction in caesarean section rates compared with general birth and parenting classes. Given the rising rates of caesarean section, we aimed to determine whether there was a difference in mode of birth in women based on the type of antenatal education attended. Materials and methods: A cross-sectional antenatal survey of nulliparous women ≥28 weeks gestation with a singleton pregnancy was conducted in two maternity hospitals in Sydney, Australia in 2018. Women were asked what type of antenatal education they attended and sent a follow-up survey post-birth. Hospital birth data were also obtained. Education was classified into four groups: psychoprophylaxis, birth and parenting, other, or none. Results: Five hundred and five women with birth data were included. A higher proportion of women who attended psychoprophylaxis education had a vaginal birth (instrumental/spontaneous) (79%) compared with women who attended birth and parenting, other or no education (69%, 67%, 60%, respectively P = 0.045). After adjusting for maternal characteristics, birth and hospital factors, the association was attenuated (odds ratio 2.03; 95% CI 0.93–4.43). Conclusions: Women who attended psychoprophylaxis couple-based education had a trend toward higher rates of vaginal birth. Randomised trials comparing different types of antenatal education are required to determine whether psychoprophylaxis education can reduce caesarean section rates and improve other birth outcomes

Increased incidence of glucose disorders during pregnancy is not explained by pre-pregnancy obesity in London, Canada

<p>Abstract</p> <p>Background</p> <p>The increasing incidence of impaired glucose tolerance (IGT), gestational diabetes (GDM) and type 2 diabetes (T2D) during pregnancy was hypothesized to be associated with increases in pre-pregnancy body mass index (BMI). The aims were to 1) determine the prevalence of IGT/GDM/T2 D over a 10 year period; 2) examine the relationship between maternal overweight/obesity and IGT/GDM/T2D; and 3) examine the extent to which maternal metabolic complications impact maternal and fetal pregnancy outcomes.</p> <p>Methods</p> <p>Data arose from a perinatal database which contains maternal characteristics and perinatal outcome for all singleton infants born in London, Canada between January 1, 2000 and December 31, 2009. Univariable and multivariable odds ratios (OR) were estimated using logistic regression with IGT/GDM/T2 D being the outcome of interest.</p> <p>Results</p> <p>A total of 36,597 women were included in the analyses. Population incidence of IGT, GDM and T2 D rose from 0.7%, 2.9% and 0.5% in 2000 to 1.2%, 4.2% and 0.9% in 2009. The univariable OR for IGT, GDM and T2 D were 1.65, 1.52 and 2.06, respectively, over the ten year period. After controlling for maternal age, parity and pre-pregnancy BMI the OR did not decrease. Although there was a positive relationship between pre-pregnancy BMI and prevalence of IGT/GDM/T2 D, this did not explain the time trends in the latter. Diagnosis of IGT/GDM/T2 D increased the risk of having an Apgar score <7 at 5 minutes, which was partially explained by gestational hypertension, high placental ratio, gestational age and large for gestational age babies.</p> <p>Conclusions</p> <p>We found a significant increase in the incidence of IGT/GDM/T2 D for the decade between 2000-2009 which was not explained by rising prevalence of maternal overweight/obesity.</p

Uncovering spatiotemporal patterns of atrophy in progressive supranuclear palsy using unsupervised machine learning

Data availability: Source data are not publicly available but non-commercial academic researcher requests may be made to the chief investigators of the seven source studies, subject to data access agreements and conditions that preserve participant anonymity. The underlying SuStaIn model code is publicly available at https://github.com/ucl-pond/pySuStaIn.68 .Supplementary data: available online at: https://academic.oup.com/braincomms/article/5/2/fcad048/7067775#398676040 .Copyright © The Author(s) 2023. To better understand the pathological and phenotypic heterogeneity of progressive supranuclear palsy and the links between the two, we applied a novel unsupervised machine learning algorithm (Subtype and Stage Inference) to the largest MRI data set to date of people with clinically diagnosed progressive supranuclear palsy (including progressive supranuclear palsy–Richardson and variant progressive supranuclear palsy syndromes). Our cohort is comprised of 426 progressive supranuclear palsy cases, of which 367 had at least one follow-up scan, and 290 controls. Of the progressive supranuclear palsy cases, 357 were clinically diagnosed with progressive supranuclear palsy–Richardson, 52 with a progressive supranuclear palsy–cortical variant (progressive supranuclear palsy–frontal, progressive supranuclear palsy–speech/language, or progressive supranuclear palsy–corticobasal), and 17 with a progressive supranuclear palsy–subcortical variant (progressive supranuclear palsy–parkinsonism or progressive supranuclear palsy–progressive gait freezing). Subtype and Stage Inference was applied to volumetric MRI features extracted from baseline structural (T1-weighted) MRI scans and then used to subtype and stage follow-up scans. The subtypes and stages at follow-up were used to validate the longitudinal consistency of subtype and stage assignments. We further compared the clinical phenotypes of each subtype to gain insight into the relationship between progressive supranuclear palsy pathology, atrophy patterns, and clinical presentation. The data supported two subtypes, each with a distinct progression of atrophy: a ‘subcortical’ subtype, in which early atrophy was most prominent in the brainstem, ventral diencephalon, superior cerebellar peduncles, and the dentate nucleus, and a ‘cortical’ subtype, in which there was early atrophy in the frontal lobes and the insula alongside brainstem atrophy. There was a strong association between clinical diagnosis and the Subtype and Stage Inference subtype with 82% of progressive supranuclear palsy–subcortical cases and 81% of progressive supranuclear palsy–Richardson cases assigned to the subcortical subtype and 82% of progressive supranuclear palsy–cortical cases assigned to the cortical subtype. The increasing stage was associated with worsening clinical scores, whilst the ‘subcortical’ subtype was associated with worse clinical severity scores compared to the ‘cortical subtype’ (progressive supranuclear palsy rating scale and Unified Parkinson’s Disease Rating Scale). Validation experiments showed that subtype assignment was longitudinally stable (95% of scans were assigned to the same subtype at follow-up) and individual staging was longitudinally consistent with 90% remaining at the same stage or progressing to a later stage at follow-up. In summary, we applied Subtype and Stage Inference to structural MRI data and empirically identified two distinct subtypes of spatiotemporal atrophy in progressive supranuclear palsy. These image-based subtypes were differentially enriched for progressive supranuclear palsy clinical syndromes and showed different clinical characteristics. Being able to accurately subtype and stage progressive supranuclear palsy patients at baseline has important implications for screening patients on entry to clinical trials, as well as tracking disease progression.W.J.S. is supported by a Wellcome Trust Clinical PhD fellowship (220582/Z/20/Z). C.S. is supported by the UK Research and Innovation Medical Research Council (MR/S03546X/1). M.B. is supported by a fellowship award from the Alzheimer’s Society, UK (AS-JF-19a-004-517), and the UK Dementia Research Institute. D.M.C. is supported by the UK Dementia Research Institute, as well as Alzheimer’s Research UK (ARUK-PG2017-1946), and the University College London/University College London Hospitals, National Institute for Health and Care Research Biomedical Research Centre. H.H. is supported by the National Institutes of Health (R01AG038791, U19AG063911). A.L.Y. is supported by a Skills Development Fellowship from the Medical Research Council (MR/T027800/1). N.P.O. is a UK Research and Innovation Future Leaders Fellow (MR/S03546X/1). L.V.V. is supported by the National Institutes of Health (R01AG038791, K23AG073514) and the Alzheimer’s Association. D.C.A. is supported by the Engineering and Physical Sciences Research Council (EP/M020533/1), Medical Research Council (MR/T046422/1), and Wellcome Trust (UNS113739). J.B.R. is supported by the Wellcome Trust (220258), National Institute for Health and Care Research Cambridge Biomedical Research Centre (BRC-1215-20014), PSP Association, Evelyn Trust, and Medical Research Council (SUAG051 R101400). H.R.M. is supported by Parkinson’s UK, Cure Parkinson’s Trust, PSP Association, CBD Solutions, Drake Foundation, Medical Research Council, and the Michael J Fox Foundation. A.L.B. is supported by the National Institutes of Health (U19AG063911, R01AG038791, R01AG073482, and U24AG057437), the Rainwater Charitable Foundation, the Bluefield Project to Cure FTD, and the Alzheimer’s Association and the Association for Frontotemporal Degeneration. J.D.R. is supported by the Miriam Marks Brain Research UK Senior Fellowship and has received funding from a Medical Research Council Clinician Scientist Fellowship (MR/M008525/1) and the National Institute for Health and Care Research Rare Disease Translational Research Collaboration (BRC149/NS/MH). P.A.W. is supported by a Medical Research Council Skills Development Fellowship (MR/T027770/1). The Dementia Research Centre is supported by Alzheimer’s Research UK, Alzheimer’s Society, Brain Research UK, and The Wolfson Foundation. This work was supported by the National Institute for Health and Care Research University College London Hospitals Biomedical Research Centre, the Leonard Wolfson Experimental Neurology Centre (LWENC) Clinical Research Facility, and the UK Dementia Research Institute, which receives its funding from UK DRI Ltd., funded by the UK Medical Research Council, Alzheimer’s Society, and Alzheimer’s Research UK. The PROSPECT study is funded by the PSP Association and CBD Solutions. The 4-Repeat Tauopathy Neuroimaging Initiative (4RTNI) and FTLDNI are funded by the National Institutes of Health Grant (R01 AG038791) and through generous contributions from the Tau Research Consortium. Both are coordinated through the University of California, San Francisco, Memory and Aging Center. 4RTNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California

Clinical Utility of Random Anti–Tumor Necrosis Factor Drug–Level Testing and Measurement of Antidrug Antibodies on the Long-Term Treatment Response in Rheumatoid Arthritis

Objective: To investigate whether antidrug antibodies and/or drug non-trough levels predict the long-term treatment response in a large cohort of patients with rheumatoid arthritis (RA) treated with adalimumab or etanercept and to identify factors influencing antidrug antibody and drug levels to optimize future treatment decisions.  Methods: A total of 331 patients from an observational prospective cohort were selected (160 patients treated with adalimumab and 171 treated with etanercept). Antidrug antibody levels were measured by radioimmunoassay, and drug levels were measured by enzyme-linked immunosorbent assay in 835 serial serum samples obtained 3, 6, and 12 months after initiation of therapy. The association between antidrug antibodies and drug non-trough levels and the treatment response (change in the Disease Activity Score in 28 joints) was evaluated.  Results: Among patients who completed 12 months of followup, antidrug antibodies were detected in 24.8% of those receiving adalimumab (31 of 125) and in none of those receiving etanercept. At 3 months, antidrug antibody formation and low adalimumab levels were significant predictors of no response according to the European League Against Rheumatism (EULAR) criteria at 12 months (area under the receiver operating characteristic curve 0.71 [95% confidence interval (95% CI) 0.57, 0.85]). Antidrug antibody–positive patients received lower median dosages of methotrexate compared with antidrug antibody–negative patients (15 mg/week versus 20 mg/week; P = 0.01) and had a longer disease duration (14.0 versus 7.7 years; P = 0.03). The adalimumab level was the best predictor of change in the DAS28 at 12 months, after adjustment for confounders (regression coefficient 0.060 [95% CI 0.015, 0.10], P = 0.009). Etanercept levels were associated with the EULAR response at 12 months (regression coefficient 0.088 [95% CI 0.019, 0.16], P = 0.012); however, this difference was not significant after adjustment. A body mass index of ≥30 kg/m2 and poor adherence were associated with lower drug levels.  Conclusion: Pharmacologic testing in anti–tumor necrosis factor–treated patients is clinically useful even in the absence of trough levels. At 3 months, antidrug antibodies and low adalimumab levels are significant predictors of no response according to the EULAR criteria at 12 months

A core outcome set for pre‐eclampsia research: an international consensus development study

Objective To develop a core outcome set for pre‐eclampsia. Design Consensus development study. Setting International. Population Two hundred and eight‐one healthcare professionals, 41 researchers and 110 patients, representing 56 countries, participated. Methods Modified Delphi method and Modified Nominal Group Technique. Results A long‐list of 116 potential core outcomes was developed by combining the outcomes reported in 79 pre‐eclampsia trials with those derived from thematic analysis of 30 in‐depth interviews of women with lived experience of pre‐eclampsia. Forty‐seven consensus outcomes were identified from the Delphi process following which 14 maternal and eight offspring core outcomes were agreed at the consensus development meeting. Maternal core outcomes: death, eclampsia, stroke, cortical blindness, retinal detachment, pulmonary oedema, acute kidney injury, liver haematoma or rupture, abruption, postpartum haemorrhage, raised liver enzymes, low platelets, admission to intensive care required, and intubation and ventilation. Offspring core outcomes: stillbirth, gestational age at delivery, birthweight, small‐for‐gestational‐age, neonatal mortality, seizures, admission to neonatal unit required and respiratory support. Conclusions The core outcome set for pre‐eclampsia should underpin future randomised trials and systematic reviews. Such implementation should ensure that future research holds the necessary reach and relevance to inform clinical practice, enhance women's care and improve the outcomes of pregnant women and their babies