Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015

Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric history, lack of validation, and restriction to a single classifier (logistic regression). Consequently, predictive performance remains low, and risk quantification has not been adopted into antenatal practice. The study population consisted of all births to women in Western Australia from 1980 to 2015, excluding terminations. After all exclusions there were 947,025 livebirths and 5,788 stillbirths. Predictive models for stillbirth were developed using multiple machine learning classifiers: regularised logistic regression, decision trees based on classification and regression trees, random forest, extreme gradient boosting (XGBoost), and a multilayer perceptron neural network. We applied 10-fold cross-validation using independent data not used to develop the models. Predictors included maternal socio-demographic characteristics, chronic medical conditions, obstetric complications and family history in both the current and previous pregnancy. In this cohort, 66% of stillbirths were observed for multiparous women. The best performing classifier (XGBoost) predicted 45% (95% CI: 43%, 46%) of stillbirths for all women and 45% (95% CI: 43%, 47%) of stillbirths after the inclusion of previous pregnancy history. Almost half of stillbirths could be potentially identified antenatally based on a combination of current pregnancy complications, congenital anomalies, maternal characteristics, and medical history. Greatest sensitivity is achieved with addition of current pregnancy complications. Ensemble classifiers offered marginal improvement for prediction compared to logistic regression

Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015

Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric history, lack of validation, and restriction to a single classifier (logistic regression). Consequently, predictive performance remains low, and risk quantification has not been adopted into antenatal practice. The study population consisted of all births to women in Western Australia from 1980 to 2015, excluding terminations. After all exclusions there were 947,025 livebirths and 5,788 stillbirths. Predictive models for stillbirth were developed using multiple machine learning classifiers: regularised logistic regression, decision trees based on classification and regression trees, random forest, extreme gradient boosting (XGBoost), and a multilayer perceptron neural network. We applied 10-fold cross-validation using independent data not used to develop the models. Predictors included maternal socio-demographic characteristics, chronic medical conditions, obstetric complications and family history in both the current and previous pregnancy. In this cohort, 66% of stillbirths were observed for multiparous women. The best performing classifier (XGBoost) predicted 45% (95% CI: 43%, 46%) of stillbirths for all women and 45% (95% CI: 43%, 47%) of stillbirths after the inclusion of previous pregnancy history. Almost half of stillbirths could be potentially identified antenatally based on a combination of current pregnancy complications, congenital anomalies, maternal characteristics, and medical history. Greatest sensitivity is achieved with addition of current pregnancy complications. Ensemble classifiers offered marginal improvement for prediction compared to logistic regression

Значение мотивации персонала на предприятии

Основная цель – систематизировать сведения о мотивации персонала и его значении на предприятии

Factors influencing gastrointestinal tract and microbiota immune interaction in preterm infants

The role of microbial colonization is indispensable for keeping a balanced immune response in life. However, the events that regulate the establishment of the microbiota, their timing, and the way in which they interact with the host are not yet fully understood. Factors such as gestational age, mode of delivery, environment, hygienic measures, and diet influence the establishment of microbiota in the perinatal period. Environmental microbes constitute the most important group of exogenous stimuli in this critical time frame. However, the settlement of a stable gut microbiota in preterm infants is delayed compared to term infants. Preterm infants have an immature gastrointestinal tract and immune system which predisposes to infectious morbidity. Neonatal microbial dynamics and alterations in early gut microbiota may precede and/or predispose to diseases such as necrotizing enterocolitis (NEC), late-onset sepsis or others. During this critical period, nutrition is the principal contributor for immunological and metabolic development, and microbiological programming. Breast milk is a known source of molecules that act synergistically to protect the gut barrier and enhance the maturation of the gut-related immune response. Host-microbe interactions in preterm infants and the protective role of diet focused on breast milk impact are beginning to be unveiled.M.C. acknowledges a “Rio Hortega” Research Fellowship Grant (CM13/0017) and M.V. acknowledges grants PI11/0313 and RD12/0026/0012 (Red SAMID) from the Instituto Carlos III (Spanish Ministry of Economy and Competitivity). M.C.C. and G.P-M. were supported by the grant AGL2013-47420-R from the Spanish Ministry of Science and Innovation.Peer reviewe

Evidence-based guidelines for use of probiotics in preterm neonates

<p>Abstract</p> <p>Background</p> <p>Current evidence indicates that probiotic supplementation significantly reduces all-cause mortality and definite necrotising enterocolitis without significant adverse effects in preterm neonates. As the debate about the pros and cons of routine probiotic supplementation continues, many institutions are satisfied with the current evidence and wish to use probiotics routinely. Because of the lack of detail on many practical aspects of probiotic supplementation, clinician-friendly guidelines are urgently needed to optimise use of probiotics in preterm neonates.</p> <p>Aim</p> <p>To develop evidence-based guidelines for probiotic supplementation in preterm neonates.</p> <p>Methods</p> <p>To develop core guidelines on use of probiotics, including strain selection, dose and duration of supplementation, we primarily used the data from our recent updated systematic review of randomised controlled trials. For equally important issues including strain identification, monitoring for adverse effects, product format, storage and transport, and regulatory hurdles, a comprehensive literature search, covering the period 1966-2010 without restriction on the study design, was conducted, using the databases PubMed and EMBASE, and the proceedings of scientific conferences; these data were used in our updated systematic review.</p> <p>Results</p> <p>In this review, we present guidelines, including level of evidence, for the practical aspects (for example, strain selection, dose, duration, clinical and laboratory surveillance) of probiotic supplementation, and for dealing with non-clinical but important issues (for example, regulatory requirements, product format). Evidence was inadequate in some areas, and these should be a target for further research.</p> <p>Conclusion</p> <p>We hope that these evidence-based guidelines will help to optimise the use of probiotics in preterm neonates. Continued research is essential to provide answers to the current gaps in knowledge about probiotics.</p

Microbiota and neurologic diseases : potential effects of probiotics

Background: The microbiota colonizing the gastrointestinal tract have been associated with both gastrointestinal and extra-gastrointestinal diseases. In recent years, considerable interest has been devoted to their role in the development of neurologic diseases, as many studies have described bidirectional communication between the central nervous system and the gut, the so-called "microbiota-gut-brain axis". Considering the ability of probiotics (i.e., live non-pathogenic microorganisms) to restore the normal microbial population and produce benefits for the host, their potential effects have been investigated in the context of neurologic diseases. The main aims of this review are to analyse the relationship between the gut microbiota and brain disorders and to evaluate the current evidence for the use of probiotics in the treatment and prevention of neurologic conditions. Discussion: Overall, trials involving animal models and adults have reported encouraging results, suggesting that the administration of probiotic strains may exert some prophylactic and therapeutic effects in a wide range of neurologic conditions. Studies involving children have mainly focused on autism spectrum disorder and have shown that probiotics seem to improve neuro behavioural symptoms. However, the available data are incomplete and far from conclusive. Conclusions: The potential usefulness of probiotics in preventing or treating neurologic diseases is becoming a topic of great interest. However, deeper studies are needed to understand which formulation, dosage and timing might represent the optimal regimen for each specific neurologic disease and what populations can benefit. Moreover, future trials should also consider the tolerability and safety of probiotics in patients with neurologic diseases

Risk of Mortality into Adulthood According to Gestational Age at Birth

© 2017 Objectives To quantify the independent risks of neonatal (0-28 days), postneonatal (29-364 days), 1- to 5- and 6- to 30-year mortality by gestational age and investigate changes in survival over time in an Australian birth cohort. Study design Maternal and birth related Western Australian population data (1980-2010) were linked to the state mortality data using a retrospective cohort study design involving 722 399 live-born singletons infants. Results When compared with 39- to 41-week born infants, the adjusted risk ratio for neonatal mortality was 124.8 (95% CI 102.9-151.3) for 24-31 weeks of gestation, 3.4 (95% CI 2.4-4.7) for 35-36 weeks of gestation, and 1.4 (95% CI 1.1-1.8) for 37-38 weeks of gestation. For 24-31 weeks of gestation infants, the adjusted hazard ratio for postneonatal mortality (29-364 days) was 13.9 (95% CI 10.9-17.6), for 1- to 5-year mortality 1.4 (95% CI 0.7-3.0) and for 6- to 30-year mortality 1.3 (95% CI 0.8-2.3). The risk of neonatal and postneonatal mortality for those born preterm decreased over time. Conclusions In Western Australia, late preterm and early term infants experienced higher risk of neonatal and postneonatal mortality when compared with their full-term peers. There was insufficient evidence to show that gestational length was independently associated with mortality beyond 1 year of age. Neonatal and postneonatal mortality improved with each decade of the study period

Predicting long-term survival without major disability for infants born preterm

Objective: To describe the long-term neurodevelopmental and cognitive outcomes for children born preterm. Study design: In this retrospective cohort study, information on children born in Western Australia between 1983 and 2010 was obtained through linkage to population databases on births, deaths, and disabilities. For the purpose of this study, disability was defined as a diagnosis of intellectual disability, autism, or cerebral palsy. The Kaplan–Meier method was used to estimate the probability of disability-free survival up to age 25 years by gestational age. The effect of covariates and predicted survival was examined using parametric survival models. Results: Of the 720 901 recorded live births, 12 083 children were diagnosed with disability, and 5662 died without any disability diagnosis. The estimated probability of disability-free survival to 25 years was 4.1% for those born at gestational age 22 weeks, 19.7% for those born at 23 weeks, 42.4% for those born at 24 weeks, 53.0% for those born at 25 weeks, 78.3% for those born at 28 weeks, and 97.2% for those born full term (39-41 weeks). There was substantial disparity in the predicted probability of disability-free survival for children born at all gestational ages by birth profile, with 5-year estimates of 4.9% and 10.4% among Aboriginal and Caucasian populations, respectively, born at 24-27 weeks and considered at high risk (based on low Apgar score, male sex, low sociodemographic status, and remote region of residence) and 91.2% and 93.3%, respectively, for those at low risk (ie, high Apgar score, female sex, high sociodemographic status, residence in a major city). Conclusions: Apgar score, birth weight, sex, socioeconomic status, and maternal ethnicity, in addition to gestational age, have pronounced impacts on disability-free survival

Prevalence estimates of mental health problems in children and adolescents with intellectual disability: A systematic review and meta-analysis

Background: Children and adolescents with intellectual disability are at risk of developing psychiatric symptoms and disorders; yet, the estimates reported in the literature have been inconsistent, presenting a potential barrier for service planning and delivery. Sources of variability could arise from differences in measurement instruments as well as subgroup membership by severity of intellectual disability, gender and age. This systematic review aimed to address these gaps. Method: MEDLINE and PsycINFO databases were searched from inception to 2018 and selected studies were reviewed. Studies were included if they reported point prevalence estimates of mental health symptomology or diagnoses in a general population of 6- to 21-year-old individuals with intellectual disability. The Joanna Briggs Institute Prevalence Critical Appraisal Checklist was applied to eligible papers to appraise their scientific strength. Pooled prevalence for mental health symptomology was determined using a random-effects meta-analysis. Results: A total of 19 studies were included, including 6151 children and adolescents. The pooled prevalence estimate captured by the Developmental Behaviour Checklist was 38% (95% confidence interval = [31, 46]), contrasting with 49% (95% confidence interval = [46, 51]) captured by the Child Behaviour Checklist; both rates were higher than a non-intellectual disability population. Severity of intellectual disability did not significantly influence the Developmental Behaviour Checklist risks. Insufficient data were available to conduct statistical analyses on the effects of age, gender and socioeconomic status. Of diagnosed psychiatric disorders, attention deficit/hyperactivity disorder (30%), conduct disorder (3–21%) and anxiety disorders (7–34%) were the most prevalent conditions. Conclusion: This review consists of the largest sample hitherto evaluated. In the intellectual disability population, mental health comorbidities could be better detected by a symptom phenotype than a psychiatric diagnostic phenotype. Crucially, future research needs to address the effect of measurement validity in the intellectual disability population. Estimated prevalence rates were high compared to the general population, indicating the importance of systematic screening, case detection and appropriate management