Preterm birth therapies to target inflammation

Preterm birth (PTB; defined as delivery before 37 weeks of pregnancy) is the leading cause of morbidity and mortality in infants and children aged <5 years, conferring potentially devastating short‐ and long‐term complications. Despite extensive research in the field, there is currently a paucity of medications available for PTB prevention and treatment. Over the past few decades, inflammation in gestational tissues has emerged at the forefront of PTB pathophysiology. Even in the absence of infection, inflammation alone can prematurely activate the main components of parturition resulting in uterine contractions, cervical ripening and dilatation, membrane rupture, and subsequent PTB. Mechanistic studies have identified critical elements of the complex inflammatory molecular pathways involved in PTB. Here, we discuss therapeutic options that target such key mediators with an aim to prevent, postpone, or treat PTB. We provide an overview of more traditional therapies that are currently used or being tested in humans, and we highlight recent advances in preclinical studies introducing novel approaches with therapeutic potential. We conclude that urgent collaborative action is required to address the unmet need of developing effective strategies to tackle the challenge of PTB and its complications

Long term cognitive outcomes of early term (37-38 weeks) and late preterm (34-36 weeks) births: a systematic review

Background: There is a paucity of evidence regarding long-term outcomes of late preterm (34-36 weeks) and early term (37-38 weeks) delivery.  The objective of this systematic review was to assess long-term cognitive outcomes of children born at these gestations. Methods: Four electronic databases (Medline, Embase, clinicaltrials.gov and PsycINFO) were searched.  Last search was 5 th August 2016.  Studies were included if they reported gestational age, IQ measure and the ages assessed.  The protocol was registered with the International prospective register of systematic reviews (PROSPERO Record CRD42015015472).  Two independent reviewers assessed the studies.  Data were abstracted and critical appraisal performed of eligible papers. Results: Of 11,905 potential articles, seven studies reporting on 41,344 children were included.  For early term births, four studies (n = 35,711) consistently showed an increase in cognitive scores for infants born at full term (39-41 weeks) compared to those born at early term (37-38 weeks) with increases for each week of term (difference between 37 and 40 weeks of around 3 IQ points), despite differences in age of testing and method of IQ/cognitive testing.  Four studies (n = 5644) reporting childhood cognitive outcomes of late preterm births (34 - 36 weeks) also differed in study design (cohort and case control); age of testing; and method of IQ testing, and found no differences in outcomes between late preterm and term births, although risk of bias was high in included studies. Conclusion:  Children born at 39-41 weeks have higher cognitive outcome scores compared to those born at early term (37-38 weeks).  This should be considered when discussing timing of delivery.  For children born late preterm, the data is scarce and when compared to full term (37-42 weeks) did not show any difference in IQ scores

Gestational age at delivery of twins and perinatal outcomes : a cohort study in Aberdeen, Scotland

This work was supported by the Wellcome Trust through a PhD studentship to SRM [104490] and a Clinical Career Development Fellowship to SJS [209560]. First published: 03 Apr 2019, 4:65 (https://doi.org/10.12688/wellcomeopenres.15211.1) Latest published: 22 Jul 2019, 4:65 (https://doi.org/10.12688/wellcomeopenres.15211.2)Peer reviewedPublisher PD

Machine learning on cardiotocography data to classify fetal outcomes: A scoping review

Introduction: Uterine contractions during labour constrict maternal blood flow and oxygen delivery to the developing baby, causing transient hypoxia. While most babies are physiologically adapted to withstand such intrapartum hypoxia, those exposed to severe hypoxia or with poor physiological reserves may experience neurological injury or death during labour. Cardiotocography (CTG) monitoring was developed to identify babies at risk of hypoxia by detecting changes in fetal heart rate (FHR) patterns. CTG monitoring is in widespread use in intrapartum care for the detection of fetal hypoxia, but the clinical utility is limited by a relatively poor positive predictive value (PPV) of an abnormal CTG and significant inter and intra observer variability in CTG interpretation. Clinical risk and human factors may impact the quality of CTG interpretation. Misclassification of CTG traces may lead to both under-treatment (with the risk of fetal injury or death) or over-treatment (which may include unnecessary operative interventions that put both mother and baby at risk of complications). Machine learning (ML) has been applied to this problem since early 2000 and has shown potential to predict fetal hypoxia more accurately than visual interpretation of CTG alone. To consider how these tools might be translated for clinical practice, we conducted a review of ML techniques already applied to CTG classification and identified research gaps requiring investigation in order to progress towards clinical implementation. Materials and method: We used identified keywords to search databases for relevant publications on PubMed, EMBASE and IEEE Xplore. We used Preferred Reporting Items for Systematic Review and Meta-Analysis for Scoping Reviews (PRISMA-ScR). Title, abstract and full text were screened according to the inclusion criteria. Results: We included 36 studies that used signal processing and ML techniques to classify CTG. Most studies used an open-access CTG database and predominantly used fetal metabolic acidosis as the benchmark for hypoxia with varying pH levels. Various methods were used to process and extract CTG signals and several ML algorithms were used to classify CTG. We identified significant concerns over the practicality of using varying pH levels as the CTG classification benchmark. Furthermore, studies needed to be more generalised as most used the same database with a low number of subjects for an ML study. Conclusion: ML studies demonstrate potential in predicting fetal hypoxia from CTG. However, more diverse datasets, standardisation of hypoxia benchmarks and enhancement of algorithms and features are needed for future clinical implementation.</p