Quantitative Checklist for Autism in Toddlers (Q-CHAT). A population screening study with follow-up: the case for multiple time-point screening for autism.

ObjectiveThis is a prospective population screening study for autism in toddlers aged 18-30 months old using the Quantitative Checklist for Autism in Toddlers (Q-CHAT), with follow-up at age 4.DesignObservational study.SettingLuton, Bedfordshire and Cambridgeshire in the UK.Participants13 070 toddlers registered on the Child Health Surveillance Database between March 2008 and April 2009, with follow-up at age 4; 3770 (29%) were screened for autism at 18-30 months using the Q-CHAT and the Childhood Autism Spectrum Test (CAST) at follow-up at age 4.InterventionsA stratified sample across the Q-CHAT score distribution was invited for diagnostic assessment (phase 1). The 4-year follow-up included the CAST and the Checklist for Referral (CFR). All with CAST ≥15, phase 1 diagnostic assessment or with developmental concerns on the CFR were invited for diagnostic assessment (phase 2). Standardised diagnostic assessment at both time-points was conducted to establish the test accuracy of the Q-CHAT.Main outcome measuresConsensus diagnostic outcome at phase 1 and phase 2.ResultsAt phase 1, 3770 Q-CHATs were returned (29% response) and 121 undertook diagnostic assessment, of whom 11 met the criteria for autism. All 11 screened positive on the Q-CHAT. The positive predictive value (PPV) at a cut-point of 39 was 17% (95% CI 8% to 31%). At phase 2, 2005 of 3472 CASTs and CFRs were returned (58% response). 159 underwent diagnostic assessment, including 82 assessed in phase 1. All children meeting the criteria for autism identified via the Q-CHAT at phase 1 also met the criteria at phase 2. The PPV was 28% (95% CI 15% to 46%) after phase 1 and phase 2.ConclusionsThe Q-CHAT can be used at 18-30 months to identify autism and enable accelerated referral for diagnostic assessment. The low PPV suggests that for every true positive there would, however, be ~4-5 false positives. At follow-up, new cases were identified, illustrating the need for continued surveillance and rescreening at multiple time-points using developmentally sensitive instruments. Not all children who later receive a diagnosis of autism are detectable during the toddler period

Moderate weight change following diabetes diagnosis and 10 year incidence of cardiovascular disease and mortality

AIMS/HYPOTHESIS: Adults with type 2 diabetes are at high risk of developing cardiovascular disease (CVD). Evidence of the impact of weight loss on incidence of CVD events among adults with diabetes is sparse and conflicting. We assessed weight change in the year following diabetes diagnosis and estimated associations with 10 year incidence of CVD events and all-cause mortality.METHODS: In a cohort analysis among 725 adults with screen-detected diabetes enrolled in the Anglo-Danish-Dutch Study of Intensive Treatment in People with Screen-Detected Diabetes in Primary Care (ADDITION)-Cambridge trial, we estimated HRs for weight change in the year following diabetes diagnosis and 10 year incidence of CVD (n = 99) and all-cause mortality (n = 95) using Cox proportional hazards regression. We used linear regression to estimate associations between weight loss and CVD risk factors. Models were adjusted for age, sex, baseline BMI, smoking, occupational socioeconomic status, cardio-protective medication use and treatment group.RESULTS: Loss of ≥5% body weight in the year following diabetes diagnosis was associated with improvements in HbA1c and blood lipids and a lower hazard of CVD at 10 years compared with maintaining weight (HR 0.52 [95% CI 0.32, 0.86]). The associations between weight gain vs weight maintenance and CVD (HR 0.41 [95% CI 0.15, 1.11]) and mortality (HR 1.63 [95% CI 0.83, 3.19]) were less clear.CONCLUSIONS/INTERPRETATION: Among adults with screen-detected diabetes, loss of ≥5% body weight during the year after diagnosis was associated with a lower hazard of CVD events compared with maintaining weight. These results support the hypothesis that moderate weight loss may yield substantial long-term CVD reduction, and may be an achievable target outside of specialist-led behavioural treatment programmes.</p

Type Inference for Deadlock Detection in a Multithreaded Polymorphic Typed Assembly Language

We previously developed a polymorphic type system and a type checker for a multithreaded lock-based polymorphic typed assembly language (MIL) that ensures that well-typed programs do not encounter race conditions. This paper extends such work by taking into consideration deadlocks. The extended type system verifies that locks are acquired in the proper order. Towards this end we require a language with annotations that specify the locking order. Rather than asking the programmer (or the compiler's backend) to specifically annotate each newly introduced lock, we present an algorithm to infer the annotations. The result is a type checker whose input language is non-decorated as before, but that further checks that programs are exempt from deadlocks

First Results from Lattice Simulation of the PWMM

We present results of lattice simulations of the Plane Wave Matrix Model (PWMM). The PWMM is a theory of supersymmetric quantum mechanics that has a well-defined canonical ensemble. We simulate this theory by applying rational hybrid Monte Carlo techniques to a naive lattice action. We examine the strong coupling behaviour of the model focussing on the deconfinement transition.Comment: v3 20 pages, 8 figures, comment adde

The direct synthesis of hydrogen peroxide using a combination of a hydrophobic solvent and water

The direct synthesis of hydrogen peroxide (H2O2) has been studied using a solvent system comprising a hydrophobic alcohol (decan-1-ol) and water. It is demonstrated that, with the optimum combination of solvent and catalyst the contribution of H2O2 degradation pathways can be minimised to achieve industrially acceptable H2O2 concentrations under moderate conditions. This is achieved through the use of a catalyst that is retained by the organic component and the extraction of synthesised H2O2 into the aqueous phase, consequently limiting contact between the synthesised H2O2, catalyst and reactant gases, resulting in an improved selectivity towards H2O2. Investigation of the reaction parameters provides an insight into the proposed solvent system, and optimised conditions to produce H2O2 from molecular H2 and O2 have been identified. Through this optimisation H2O2 concentrations up to 1.9 wt% have been achieved via sequential gas replacement experiments

Predicting suicide attempts and suicide deaths among adolescents following outpatient visits

BACKGROUND: Few studies report on machine learning models for suicide risk prediction in adolescents and their utility in identifying those in need of further evaluation. This study examined whether a model trained and validated using data from all age groups works as well for adolescents or whether it could be improved. METHODS: We used healthcare data for 1.4 million specialty mental health and primary care outpatient visits among 256,823 adolescents across 7 health systems. The prediction target was 90-day risk of suicide attempt following a visit. We used logistic regression with least absolute shrinkage and selection operator (LASSO) and generalized estimating equations (GEE) to predict risk. We compared performance of three models: an existing model, a recalibrated version of that model, and a newly-learned model. Models were compared using area under the receiver operating curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value. RESULTS: The AUC produced by the existing model for specialty mental health visits estimated in adolescents alone (0.796; [0.789, 0.802]) was not significantly different than the AUC of the recalibrated existing model (0.794; [0.787, 0.80]) or the newly-learned model (0.795; [0.789, 0.801]). Predicted risk following primary care visits was also similar: existing (0.855; [0.844, 0.866]), recalibrated (0.85 [0.839, 0.862]), newly-learned (0.842, [0.829, 0.854]). LIMITATIONS: The models did not incorporate non-healthcare risk factors. The models relied on ICD9-CM codes for diagnoses and outcome measurement. CONCLUSIONS: Prediction models already in operational use by health systems can be reliably employed for identifying adolescents in need of further evaluation

Optimisation and application of a novel method to identify bacteriophage in maternal milk and infant stool identifies host-phage communities within preterm infant gut

Human milk oligosaccharides, proteins such as lactoferrin, and bacteria represent just some of the bioactive components of mothers’ breast milk (BM). Bacteriophages (viruses that infect bacteria) are an often-overlooked component of BM that can cause major changes in microbial composition and metabolism. BM bacteriophage composition has been explored in term and healthy infants, suggesting vertical transmission of bacteriophages occurs between mothers and their infants. Several important differences between term and very preterm infants (< 30 weeks gestational age) may limit this phenomenon in the latter. To better understand the link between BM bacteriophages and gut microbiomes of very preterm infants in health and disease, standardised protocols are required for isolation and characterisation from BM. In this study we use isolated nucleic acid content, bacteriophage richness and Shannon diversity to validate several parameters applicable during bacteriophage isolation from precious BM samples. Parameters validated include sample volume required; centrifugal sedimentation of microbes; hydrolysis of milk samples with digestive enzymes; induction of temperate bacteriophages and concentration / purification of isolated bacteriophage particles in donor milk (DM). Our optimised method enables characterisation of bacteriophages from as little as 0.1 mL BM. We identify viral families that were exclusively identified with inclusion of induction of temperate bacteriophages (Inoviridae) and hydrolysis of milk lipid processes (Iridoviridae and Baculoviridae). Once applied to a small clinical cohort we demonstrate vertical transmission of bacteriophages from mothers BM to the gut of very preterm infants at the species level. This optimised method will enable future research characterising the bacteriophage composition of BM in very preterm infants to determine their clinical relevance in the development of a healthy preterm infant gut microbiome

Quantitative Checklist for Autism in Toddlers (Q-CHAT). A population screening study with follow-up: the case for multiple time-point screening for autism

Funder: Autism Research TrustFunder: National Lottery Community Fund; FundRef: http://dx.doi.org/10.13039/501100013529Funder: Collaboration for Leadership in Applied Health Research and Care (CLAHRC)Objective: This is a prospective population screening study for autism in toddlers aged 18–30 months old using the Quantitative Checklist for Autism in Toddlers (Q-CHAT), with follow-up at age 4. Design: Observational study. Setting: Luton, Bedfordshire and Cambridgeshire in the UK. Participants: 13 070 toddlers registered on the Child Health Surveillance Database between March 2008 and April 2009, with follow-up at age 4; 3770 (29%) were screened for autism at 18–30 months using the Q-CHAT and the Childhood Autism Spectrum Test (CAST) at follow-up at age 4. Interventions: A stratified sample across the Q-CHAT score distribution was invited for diagnostic assessment (phase 1). The 4-year follow-up included the CAST and the Checklist for Referral (CFR). All with CAST ≥15, phase 1 diagnostic assessment or with developmental concerns on the CFR were invited for diagnostic assessment (phase 2). Standardised diagnostic assessment at both time-points was conducted to establish the test accuracy of the Q-CHAT. Main outcome measures: Consensus diagnostic outcome at phase 1 and phase 2. Results: At phase 1, 3770 Q-CHATs were returned (29% response) and 121 undertook diagnostic assessment, of whom 11 met the criteria for autism. All 11 screened positive on the Q-CHAT. The positive predictive value (PPV) at a cut-point of 39 was 17% (95% CI 8% to 31%). At phase 2, 2005 of 3472 CASTs and CFRs were returned (58% response). 159 underwent diagnostic assessment, including 82 assessed in phase 1. All children meeting the criteria for autism identified via the Q-CHAT at phase 1 also met the criteria at phase 2. The PPV was 28% (95% CI 15% to 46%) after phase 1 and phase 2. Conclusions: The Q-CHAT can be used at 18–30 months to identify autism and enable accelerated referral for diagnostic assessment. The low PPV suggests that for every true positive there would, however, be ~4–5 false positives. At follow-up, new cases were identified, illustrating the need for continued surveillance and rescreening at multiple time-points using developmentally sensitive instruments. Not all children who later receive a diagnosis of autism are detectable during the toddler period

Long-term effects of intensive multifactorial therapy in individuals with screen-detected type 2 diabetes in primary care:10-year follow-up of the ADDITION-Europe cluster-randomised trial

BACKGROUND: The multicentre, international ADDITION-Europe study investigated the effect of promoting intensive treatment of multiple risk factors among people with screen-detected type 2 diabetes over 5 years. Here we report the results of a post-hoc 10-year follow-up analysis of ADDITION-Europe to establish whether differences in treatment and cardiovascular risk factors have been maintained and to assess effects on cardiovascular outcomes.METHODS: As previously described, general practices from four centres (Denmark, Cambridge [UK], Leicester [UK], and the Netherlands) were randomly assigned by computer-generated list to provide screening followed by routine care of diabetes, or screening followed by intensive multifactorial treatment. Population-based stepwise screening programmes among people aged 40-69 years (50-69 years in the Netherlands), between April, 2001, and December, 2006, identified patients with type 2 diabetes. Allocation was concealed from patients. Following the 5-year follow-up, no attempts were made to maintain differences in treatment between study groups. In this report, we did a post-hoc analysis of cardiovascular and renal outcomes over 10 years following randomisation, including a 5 years post-intervention follow-up. As in the original trial, the primary endpoint was a composite of first cardiovascular event, including cardiovascular mortality, cardiovascular morbidity (non-fatal myocardial infarction and non-fatal stroke), revascularisation, and non-traumatic amputation, up to Dec 31, 2014. Analyses were based on the intention-to-treat principle. ADDITION-Europe is registered with ClinicalTrials.gov, NCT00237549.FINDINGS: 343 general practices were randomly assigned to routine diabetes care (n=176) or intensive multifactorial treatment (n=167). 317 of these general practices (157 in the routine care group, 161 in the intensive treatment group) included eligible patients between April, 2001, and December, 2006. Of the 3233 individuals with screen-detected diabetes, 3057 agreed to participate (1379 in the routine care group, 1678 in the intensive treatment group), but at the 10-year follow-up 14 were lost to follow-up and 12 withdrew, leaving 3031 to enter 10-year follow-up analysis. Mean duration of follow-up was 9·61 years (SD 2·99). Sustained reductions over 10 years following diagnosis were apparent for bodyweight, HbA1c, blood pressure, and cholesterol in both study groups, but between-group differences identified at 1 and 5 years were attenuated at the 10-year follow-up. By 10 years, 443 participants had a first cardiovascular event and 465 died. There was no significant difference between groups in the incidence of the primary composite outcome (16·1 per 1000 person-years in the routine care group vs 14·3 per 1000 person-years in the intensive treatment group; hazard ratio [HR] 0·87, 95% CI 0·73-1·04; p=0·14) or all-cause mortality (15·6 vs 14·3 per 1000 person-years; HR 0·90, 0·76-1·07).INTERPRETATION: Sustained reductions in glycaemia and related cardiovascular risk factors over 10 years among people with screen-detected diabetes managed in primary care are achievable. The differences in prescribed treatment and cardiovascular risk factors in the 5 years following diagnosis were not maintained at 10 years, and the difference in cardiovascular events and mortality remained non-significant.FUNDING: National Health Service Denmark, Danish Council for Strategic Research, Danish Research Foundation for General Practice, Novo Nordisk, Novo Nordisk Foundation, Danish Centre for Evaluation and Health Technology Assessment, Danish National Board of Health, Danish Medical Research Council, Aarhus University Research Foundation, Astra, Pfizer, GlaxoSmithKline, Servier, HemoCue, Wellcome Trust, UK Medical Research Council, UK National Institute for Health Research, UK National Health Service, Merck, Julius Center for Health Sciences and Primary Care, UK Department of Health, and Nuts-OHRA.</p

Complex modeling with detailed temporal predictors does not improve health records-based suicide risk prediction

Suicide risk prediction models can identify individuals for targeted intervention. Discussions of transparency, explainability, and transportability in machine learning presume complex prediction models with many variables outperform simpler models. We compared random forest, artificial neural network, and ensemble models with 1500 temporally defined predictors to logistic regression models. Data from 25,800,888 mental health visits made by 3,081,420 individuals in 7 health systems were used to train and evaluate suicidal behavior prediction models. Model performance was compared across several measures. All models performed well (area under the receiver operating curve [AUC]: 0.794-0.858). Ensemble models performed best, but improvements over a regression model with 100 predictors were minimal (AUC improvements: 0.006-0.020). Results are consistent across performance metrics and subgroups defined by race, ethnicity, and sex. Our results suggest simpler parametric models, which are easier to implement as part of routine clinical practice, perform comparably to more complex machine learning methods