Con Prof Frans Henskens

A modified case-based reasoning method is introduced aimed to fulfill the need for a triage tool that differentiates likely psychiatric diagnoses and associated risk level. Clinical cases are represented as a set of clinical features rated on a numerical scale according to level of severity. One standard case is used for each diagnostic category, represented as a vector denoting the expected severity of each clinical feature. A new case represented as another vector denoting the severity of observed clinical features in a patient is assessed against the standard cases. Measurement based on orthogonal vector projection was used as a clinically intuitive measurement of similarity. Using thirty different test cases representing six different diagnostic categories, this measure and alternative similarity measures consisting of cosine similarity and Euclidean distance were evaluated. Results indicated that orthogonal vector projection was superior to the other two methods in differentiating diagnoses and predicting severity.

Mental state examination (MSE) involves assessing the overall severity of illness and also differentiating likely diagnoses. When it is performed on patients serially during the period of their illness, the consecutive estimates can serve as an important way to track their recovery. However, the traditional approach to mental state examination that uses clinician's subjective judgement and results in subjective estimates can be unreliable and prone to inconsistencies. Using the approach introduced in this paper, a case is represented as a vector of thirty five different clinical features, which are rated using a numerical scale according to the severity of each clinical feature. The vector length is used as a measure of the overall severity of the illness. The case base consists of one standard case for each of 6 diagnostic categories. Each standard case represents a typical case for its diagnostic category, with each clinical feature rated according to the maximum level of severity that can be expected for that category. Evaluation of a given clinical case, with clinical features as rated by a clinician with regard to the likely diagnoses involves measuring the similarity of the resulting case vector with the standard vectors in the case base. Whilst cosine similarity and Euclidean distance are alternative measures of similarity, a more clinically intuitive and accurate measure based on orthogonal vector projection is proposed. The orthogonal vector projection approach to case based assessment was evaluated using thirty different test cases representing six different common diagnostic categories. For each of the test cases similarity measures obtained using orthogonal vector projection were compared with measures obtained using cosine similarity and Euclidean distance. The results indicated that the orthogonal vector projection approach was able to differentiate both the diagnosis and severity of illness more accurately than the other two similarity measures. The proposed approach has the potential to be used as a standardised clinical tool for both establishing the diagnosis and severity of illness, and also measuring the recovery from illness. In particular, the estimates of recovery obtained from this approach can serve as an important index in healthcare economics.

This paper discusses the needs of medical researchers working in the area of patient-centred medicine, in particular their use of survey data in measuring patient opinions, needs, perceived quality of care received, and priorities of health service interventions. Until quite recently, collection of survey data has been either paper-based, or achieved using computer software that largely duplicated paper-based processes with limited additional functionality. The authors investigate the use of web-based technology to support collection of such data from patients, including experiences and observations on enhanced/additional functionality made possible by its adoption. A novel software design termed QuON is presented, together with examples of its capabilities and uses in current research projects. Copyright © 2014 SCITEPRESS - Science and Technology Publications. All rights reserved.

Structural neuroimaging studies have identified a combination of shared and disorder-specific patterns of gray matter (GM) deficits across psychiatric disorders. Pooling large data allows for examination of a possible common neuroanatomical basis that may identify a certain vulnerability for mental illness. Large-scale collaborative research is already facilitated by data repositories, institutionally supported databases, and data archives. However, these data-sharing methodologies can suffer from significant barriers. Federated approaches augment these approaches by enabling access or more sophisticated, shareable and scaled-up analyses of large-scale data. We examined GM alterations using Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation, an open-source, decentralized analysis application. Through federated analysis of eight sites, we identified significant overlap in the GM patterns (n = 4,102) of individuals with schizophrenia, major depressive disorder, and autism spectrum disorder. These results show cortical and subcortical regions that may indicate a shared vulnerability to psychiatric disorders.

Stratified medicine holds great promise to tailor treatment to the needs of individual patients. While genetics holds great potential to aid¿patient stratification, it remains a major challenge to operationalize complex genetic risk factor profiles to deconstruct clinical heterogeneity. Contemporary approaches to this problem rely on polygenic risk scores (PRS), which provide only limited clinical utility and lack a clear biological foundation. To overcome these limitations, we develop the CASTom-iGEx approach to stratify individuals based on the aggregated impact of their genetic risk factor profiles on tissue specific gene expression levels. The paradigmatic application of this approach to coronary artery disease or schizophrenia patient cohorts identified diverse strata or biotypes. These biotypes are characterized by distinct endophenotype profiles as well as clinical parameters and are fundamentally distinct from PRS based groupings. In stark contrast to the latter, the CASTom-iGEx strategy discovers biologically meaningful and clinically actionable patient subgroups, where complex genetic liabilities are not randomly distributed across individuals but rather converge onto distinct disease relevant biological processes. These results support the notion of different patient biotypes characterized by partially distinct pathomechanisms. Thus, the universally applicable approach presented here has the potential to constitute an important component of future personalized medicine paradigms.

Genetics have nominated many schizophrenia risk genes and identified convergent signals between schizophrenia and neurodevelopmental disorders. However, functional interpretation of the nominated genes in the relevant brain cell types is often lacking. We executed interaction proteomics for six schizophrenia risk genes that have also been implicated in neurodevelopment in human induced cortical neurons. The resulting protein network is enriched for common variant risk of schizophrenia in Europeans and East Asians, is down-regulated in layer 5/6 cortical neurons of individuals affected by schizophrenia, and can complement fine-mapping and eQTL data to prioritize additional genes in GWAS loci. A sub-network centered on HCN1 is enriched for common variant risk and contains proteins (HCN4 and AKAP11) enriched for rare protein-truncating mutations in individuals with schizophrenia and bipolar disorder. Our findings showcase brain cell-type-specific interactomes as an organizing framework to facilitate interpretation of genetic and transcriptomic data in schizophrenia and its related disorders.

Schizophrenia (SCZ) is a chronic mental illness and among the most debilitating conditions encountered in medical practice. A recent landmark SCZ study of the protein-coding regions of the genome identified a causal role for ten genes and a concentration of rare variant signals in evolutionarily constrained genes1. This recent study¿and most other large-scale human genetics studies¿was mainly composed of individuals of European (EUR) ancestry, and the generalizability of the findings in non-EUR populations remains unclear. To address this gap, we designed a custom sequencing panel of 161 genes selected based on the current knowledge of SCZ genetics and sequenced a new cohort of 11,580 SCZ cases and 10,555 controls of diverse ancestries. Replicating earlier work, we found that cases carried a significantly higher burden of rare protein-truncating variants (PTVs) among evolutionarily constrained genes (odds ratio = 1.48; P = 5.4 × 10-6). In meta-analyses with existing datasets totaling up to 35,828 cases and 107,877 controls, this excess burden was largely consistent across five ancestral populations. Two genes (SRRM2 and AKAP11) were newly implicated as SCZ risk genes, and one gene (PCLO) was identified as shared by individuals with SCZ and those with autism. Overall, our results lend robust support to the rare allelic spectrum of the genetic architecture of SCZ being conserved across diverse human populations.

While germline copy-number variants (CNVs) contribute to schizophrenia (SCZ) risk, the contribution of somatic CNVs (sCNVs)¿present in some but not all cells¿remains unknown. We identified sCNVs using blood-derived genotype arrays from 12,834 SCZ cases and 11,648 controls, filtering sCNVs at loci recurrently mutated in clonal blood disorders. Likely early-developmental sCNVs were more common in cases (0.91%) than controls (0.51%, p = 2.68e-4), with recurrent somatic deletions of exons 1¿5 of the NRXN1 gene in five SCZ cases. Hi-C maps revealed ectopic, allele-specific loops forming between a potential cryptic promoter and non-coding cis-regulatory elements upon 5' deletions in NRXN1. We also observed recurrent intragenic deletions of ABCB11, encoding a transporter implicated in anti-psychotic response, in five treatment-resistant SCZ cases and showed that ABCB11 is specifically enriched in neurons forming mesocortical and mesolimbic dopaminergic projections. Our results indicate potential roles of sCNVs in SCZ risk.

Schizophrenia has a heritability of 60¿80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies.

The concept of a smart factory, under Industry 4.0 relies heavily on cyber physical systems (CPS) and intra-enterprise-wide-networks (IWN). Cloud-based implementation is incumbent to accomplish the promises of enterprise integration, automation, seamless information exchange and intelligent self-organisation. Extensive research has been conducted in this domain, however, there is still much research to be done from the perspective of such frameworks in small to medium size enterprises (SMEs). In this context, the agent-oriented smart factory (AOSF) framework provides a generic end-to-end supply chain (SC) model, compliant with CPS and Industry 4.0 standards. In order to support the crucial side of warehouse management, this paper presents AOSF's recommended agent-oriented storage and retrieval (AOSR) warehouse planner with hybrid logic-based strategy, which yields a smart time-stamped plan to manage product placement and retrieval efficiently. The AOSF-associated AOSR-planner uses the hierarchical task network (HTN) AI planning to ensure different warehouse operations in a timely manner.

Nowadays Model-Driven Engineering (MDE) is gaining more popularity due to high-level development leading to a faster generation of executable code, which reduces manual intervention. Verification is crucial at different levels of model-based development. Model-based development, along with formal verification process, assures the developed model satisfies software requirements described in formal specifications. Owing the inadequate knowledge of formal methods (complex mathematical theory), software developers are not adopting formal methods during software development. There are several approaches in the literature available to transform MDE models into formal models directly for formal verification, and these approaches require an additional input of formal specifications to verification tools for formal verification. But these methods have not addressed the problem of formal specifications at the model level. In this paper, we design a modelling framework using modelling techniques, which allows specifying formal properties at the model level, automatically extracting formal specifications and formal models from developed application models, which are used for formal verification. The proposed method allows full automation and reduces the time for formal verification process during the development life-cycle. Furthermore, the method reduces the complexity of learning formal specification notations (specifications specified at the model level are automatically converted into formal specifications), which are required to input verification tools for formal verification.

The Fourth Industrial Revolution (Industry 4.0), with the help of cyber-physical systems (CPS), the Internet of Things (IoT), and Artificial Intelligence (AI), is transforming the way industrial setups are designed. Recent literature has provided insight about large firms gaining benefits from Industry 4.0, but many of these benefits do not translate to SMEs. The agent-oriented smart factory (AOSF) framework provides a solution to help bridge the gap between Industry 4.0 frameworks and SME-oriented setups by providing a general and high-level supply chain (SC) framework and an associated agent-oriented storage and retrieval (AOSR)-based warehouse management strategy. This paper presents the extended heuristics of the AOSR algorithm and details how it improves the performance efficiency in an SME-oriented warehouse. A detailed discussion on the thorough validation via scenario-based experimentation and test cases explain how AOSR yielded 60¿148% improved performance metrics in certain key areas of a warehouse.

Nowadays, automation can be assisted by using programmable logic controllers (PLCs). PLCs are typically programmed with IEC 61131-3 languages to automate and implement the applications. PLC program classification plays an important role in the identification of similar functionality, which can be considered as software clones. In this paper, we present work to identify clones in IEC 61131-3 languages, using an approach based on four different perspectives: (a) clone prediction: Filtering based on heuristics; (b) structural analysis: Detect syntactic code clones; (c) semantic analysis: Analysis of output variable dependency and input variable impact usage to detect semantic clones; (d) variable interval analysis: Analysis of each program variable intervals to examine and detect clones. Our approach is a combination of structural, semantic and data interval based analysis. As a result, our approach is feasible and yields good results in detecting clones on our test data.

Objectives: Large-scale genetic analysis of common variation in schizophrenia has been a powerful approach to understanding this complex but highly heritable psychotic disorder. To further investigate loci, genes and pathways associated more specifically in the well-characterized Australian Schizophrenia Research Bank cohort, we applied genome-wide single-nucleotide polymorphism analysis in these three annotation categories. Methods: We performed a case¿control genome-wide association study in 429 schizophrenia samples and 255 controls. Post-genome-wide association study analyses were then integrated with genomic annotations to explore the enrichment of variation at the gene and pathway level. We also examine candidate single-nucleotide polymorphisms with potential function within expression quantitative trait loci and investigate overall enrichment of variation within tissue-specific functional regulatory domains of the genome. Results: The strongest finding (p = 2.01 × 10-6, odds ratio = 1.82, 95% confidence interval = [1.42, 2.33]) in genome-wide association study was with rs10252923 at 7q21.13, downstream of FZD1 (frizzled class receptor 1). While this did not stand alone after correction, the involvement of FZD1 was supported by gene-based analysis, which exceeded the threshold for genome-wide significance (p = 2.78 × 10-6). Conclusion: The identification of FZD1, as an independent association signal at the gene level, supports the hypothesis that the Wnt signalling pathway is altered in the pathogenesis of schizophrenia and may be an important target for therapeutic development.

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed analyses of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders, identifying three groups of inter-related disorders. Meta-analysis across these eight disorders detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.

Genome-wide association studies (GWASs) are highly effective at identifying common risk variants for schizophrenia. Rare risk variants are also important contributors to schizophrenia etiology but, with the exception of large copy number variants, are difficult to detect with GWAS. Exome and genome sequencing, which have accelerated the study of rare variants, are expensive so alternative methods are needed to aid detection of rare variants. Here we re-analyze an Irish schizophrenia GWAS dataset (n = 3,473) by performing identity-by-descent (IBD) mapping followed by exome sequencing of individuals identified as sharing risk haplotypes to search for rare risk variants in coding regions. We identified 45 rare haplotypes (>1 cM) that were significantly more common in cases than controls. By exome sequencing 105 haplotype carriers, we investigated these haplotypes for functional coding variants that could be tested for association in independent GWAS samples. We identified one rare missense variant in PCNT but did not find statistical support for an association with schizophrenia in a replication analysis. However, IBD mapping can prioritize both individual samples and genomic regions for follow-up analysis but genome rather than exome sequencing may be more effective at detecting risk variants on rare haplotypes.

Background: There is considerable evidence that many complex traits have a partially shared genetic basis, termed pleiotropy. It is therefore useful to consider integrating genome-wide association study (GWAS) data across several traits, usually at the summary statistic level. A major practical challenge arises when these GWAS have overlapping subjects. This is particularly an issue when estimating pleiotropy using methods that condition the significance of one trait on the signficance of a second, such as the covariate-modulated false discovery rate (cmfdr). Results: We propose a method for correcting for sample overlap at the summary statistic level. We quantify the expected amount of spurious correlation between the summary statistics from two GWAS due to sample overlap, and use this estimated correlation in a simple linear correction that adjusts the joint distribution of test statistics from the two GWAS. The correction is appropriate for GWAS with case-control or quantitative outcomes. Our simulations and data example show that without correcting for sample overlap, the cmfdr is not properly controlled, leading to an excessive number of false discoveries and an excessive false discovery proportion. Our correction for sample overlap is effective in that it restores proper control of the false discovery rate, at very little loss in power. Conclusions: With our proposed correction, it is possible to integrate GWAS summary statistics with overlapping samples in a statistical framework that is dependent on the joint distribution of the two GWAS.

Background: The profile of cortical neuroanatomical abnormalities in schizophrenia is not fully understood, despite hundreds of published structural brain imaging studies. This study presents the first meta-analysis of cortical thickness and surface area abnormalities in schizophrenia conducted by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Schizophrenia Working Group. Methods: The study included data from 4474 individuals with schizophrenia (mean age, 32.3 years; range, 11¿78 years; 66% male) and 5098 healthy volunteers (mean age, 32.8 years; range, 10¿87 years; 53% male) assessed with standardized methods at 39 centers worldwide. Results: Compared with healthy volunteers, individuals with schizophrenia have widespread thinner cortex (left/right hemisphere: Cohen's d = -0.530/-0.516) and smaller surface area (left/right hemisphere: Cohen's d = -0.251/-0.254), with the largest effect sizes for both in frontal and temporal lobe regions. Regional group differences in cortical thickness remained significant when statistically controlling for global cortical thickness, suggesting regional specificity. In contrast, effects for cortical surface area appear global. Case-control, negative, cortical thickness effect sizes were two to three times larger in individuals receiving antipsychotic medication relative to unmedicated individuals. Negative correlations between age and bilateral temporal pole thickness were stronger in individuals with schizophrenia than in healthy volunteers. Regional cortical thickness showed significant negative correlations with normalized medication dose, symptom severity, and duration of illness and positive correlations with age at onset. Conclusions: The findings indicate that the ENIGMA meta-analysis approach can achieve robust findings in clinical neuroscience studies; also, medication effects should be taken into account in future genetic association studies of cortical thickness in schizophrenia.

Purpose: The majority of research on psychological outcomes for cancer patients has focussed on the role of individual characteristics, and disease and treatment factors. There has been very little exploration of the potential contribution of the treatment clinic to these outcomes. This study explored whether there is variation among clinics in cancer patients' psychological outcomes. Methods: Cancer outpatients were recruited from 22 medical oncology and haematology clinics in Australia. Participants completed a pen and paper survey including the Hospital Anxiety and Depression Scale (HADS), as well as sociodemographic, disease and treatment characteristics. Results: Of those eligible to participate, 4233 (82%) consented and 2811 (81% of consenters) returned the completed survey. There was no statistically significant variation in HADS depression scores across clinics. Some difference in anxiety scores derived from the HADS questionnaire between clinics (p = 0.03) was found with the percentage of between-clinic variation estimated to be 1.11%. However, once all demographic, disease and treatment predictors were adjusted for there was no statistical differences between clinics (percent of between-clinic variation = 0.53%; p = 0.1415). Conclusions: Psychological outcomes were not found to vary between clinics. Other sources of variation including patient characteristics may over-ride between-clinic variability, if it exists.

Genetic correlation is a key population parameter that describes the shared genetic architecture of complex traits and diseases. It can be estimated by current state-of-art methods, i.e., linkage disequilibrium score regression (LDSC) and genomic restricted maximum likelihood (GREML). The massively reduced computing burden of LDSC compared to GREML makes it an attractive tool, although the accuracy (i.e., magnitude of standard errors) of LDSC estimates has not been thoroughly studied. In simulation, we show that the accuracy of GREML is generally higher than that of LDSC. When there is genetic heterogeneity between the actual sample and reference data from which LD scores are estimated, the accuracy of LDSC decreases further. In real data analyses estimating the genetic correlation between schizophrenia (SCZ) and body mass index, we show that GREML estimates based on ~150,000 individuals give a higher accuracy than LDSC estimates based on ~400,000 individuals (from combined meta-data). A GREML genomic partitioning analysis reveals that the genetic correlation between SCZ and height is significantly negative for regulatory regions, which whole genome or LDSC approach has less power to detect. We conclude that LDSC estimates should be carefully interpreted as there can be uncertainty about homogeneity among combined meta-datasets. We suggest that any interesting findings from massive LDSC analysis for a large number of complex traits should be followed up, where possible, with more detailed analyses with GREML methods, even if sample sizes are lesser.

White matter abnormalities associated with schizophrenia have been widely reported, although the consistency of findings across studies is moderate. In this study, neuroimaging was used to investigate white matter pathology and its impact on whole-brain white matter connectivity in one of the largest samples of patients with schizophrenia. Fractional anisotropy (FA) and mean diffusivity (MD) were compared between patients with schizophrenia or schizoaffective disorder (n = 326) and age-matched healthy controls (n = 197). Between-group differences in FA and MD were assessed using voxel-based analysis and permutation testing. Automated whole-brain white matter fiber tracking and the network-based statistic were used to characterize the impact of white matter pathology on the connectome and its rich club. Significant reductions in FA associated with schizophrenia were widespread, encompassing more than 40% (234ml) of cerebral white matter by volume and involving all cerebral lobes. Significant increases in MD were also widespread and distributed similarly. The corpus callosum, cingulum, and thalamic radiations exhibited the most extensive pathology according to effect size. More than 50% of cortico-cortical and cortico-subcortical white matter fiber bundles comprising the connectome were disrupted in schizophrenia. Connections between hub regions comprising the rich club were disproportionately affected. Pathology did not differ between patients with schizophrenia and schizoaffective disorder and was not mediated by medication. In conclusion, although connectivity between cerebral hubs is most extensively disturbed in schizophrenia, white matter pathology is widespread, affecting all cerebral lobes and the cerebellum, leading to disruptions in the majority of the brain's fiber bundles.

Background Tobacco smoking can have negative health outcomes on recovery from surgery. Although it is recommended best practice to provide patients with advice to quit and follow-up support, provision of postdischarge support is rare. Developing an online smoking cessation program may help address this gap. Objectives This paper describes the development and pretesting of an online smoking cessation program (smoke-free recovery, SFR) tailored to the orthopaedic trauma population for use while in hospital and post-discharge. Methods Drawing on the DoTTI framework for developing an online program, the following steps were followed for program development: (1) design and development; (2) testing early iteration; (3) testing for effectiveness and (4) integration and implementation. This article describes the first two stages of SFR program development. Results SFR is a 10-module online smoking cessation program tailored for patients with orthopaedic trauma. Of the participants who completed testing early iterations, none reported any difficulties orientating themselves to the program or understanding program content. The main themes were that it was 'helpful', provision of 'help to quit' was low and SFR increased thoughts of 'staying quit post discharge'. Conclusions This study found that a theory and evidence-based approach as the basis for an online smoking cessation program for patients with orthopaedic trauma was acceptable to users. A randomised controlled trial will be conducted to examine whether the online smoking cessation program is effective in increasing smoking cessation and how it can be integrated and implemented into hospital practice (stages three and four of the DoTTI framework).

Background: Population-based registries are increasingly used to recruit patient samples for research, however, they have several limitations including low consent and participation rates, and potential selection bias. To improve access to samples for research, the utility of a new model of recruitment termed the 'Consumer Register', that allows for direct patient recruitment from hospitals, was examined. This paper reports: (i) consent rates onto the register; (ii) preferred methods and frequency of contact; and (iii) the feasibility of establishing the register, including: (a) cost per person recruited to the register; (b) the differential cost and consent rates of volunteer versus paid data collectors; and (c) participant completion rates. Methods: A cross-sectional survey was conducted in five outpatient clinics in Australia. Patients were approached by volunteers or paid data collectors and asked to complete a touch-screen electronic survey. Consenting individuals were asked to indicate their willingness and preferences for enrolment onto a research register. Descriptive statistics were used to examine patient preferences and linear regression used to model the success of volunteer versus paid data collectors. The opportunity and financial costs of establishing the register were calculated. Results: A total of 1947 patients (80.6 %) consented to complete the survey, of which, 1486 (76.3 %) completed the questionnaire. Of the completers, the majority (69.4 %, or 1032 participants) were willing to be listed on the register and preferred to be contacted by email (50.3 %). Almost 39 % of completers were willing to be contacted three or more times in a 12 month period. The annual opportunity cost of resources consumed by the register was valued at $37,187, giving an opportunity cost per person recruited to the register of $36. After amortising fixed costs, the annual financial outlay was $23,004 or $22 per person recruited to the register. Use of volunteer data collectors contributed to an annual saving of $14,183, however paid data collectors achieved significantly higher consent rates. Successful enrolment onto the register was completed for 42 % of the sample. Conclusions: A Consumer Register is a promising and feasible alternative to population-based registries, with the majority of participants willing to be contacted multiple times via low-resource methods such as email. There is an effectiveness/cost trade off in the use of paid versus volunteer data collectors.

Background: Obstructive sleep apnoea (OSA) affects up to 28 % of the adult population in Western countries. The detection and management of OSA by general practitioners (GPs) can be poor. The study aimed to examine what influence enhanced invitations had on uptake of on-line learning modules for OSA by GPs, and whether recent referrals of patients to sleep specialists influenced uptake. Methods: Practicing GPs in regional Australia were identified and randomised to receive either an enhanced or standard invitation letter to a new on-line education module for OSA. The enhanced letter included indication that the module was eligible for professional accreditation and described the prevalence and burden of sleep disorders. Some included extra emphasis if the GP had recently referred a patient for diagnostic investigation of OSA. Two reminder letters were sent. Results: Of 796 eligible GPs who received the letters, sixteen (2 %) accessed the website and four completed the modules over the four-month study period. GPs who received an enhanced invitation letter were not significantly more likely to access the website compared to GPs who received the standard invitation letter. Recent referral of a patient for diagnostic investigation was also not a significant factor in influencing use of the module. Conclusion: GP interest in on-line education about OSA appears low, and emphasis of relevant recent past patient(s) and the opportunity for professional education points was not successful in increasing engagement. There is a need to identify effective approaches to improving the detection and management of OSA in general practice.

Obstructive sleep apnea affects 20% of the adult population. Weight control is considered the best non-medical means of managing the condition, therefore improving nutritional knowledge in individuals may be an appropriate strategy. This study aimed to describe the relationship between nutritional knowledge and: (i) sleep disorder severity; (ii) body mass index; and (iii) demographic characteristics in persons suspected of obstructive sleep apnea. Nutrition knowledge scores were also compared with the general population. Consecutive newly-referred patients attending the sleep laboratory for diagnostic polysomnography were invited to participate. Those who consented (n = 97) were asked to complete a touchscreen survey. Apnea-hypopnea index to measure sleep disorder severity and anthropometric measurements were obtained from the clinic. A quarter of participants were diagnosed with severe obstructive sleep apnea; and a majority (88%) were classed as being overweight or obese. The overall mean nutrition knowledge score was 58.4 ± 11.6 (out of 93). Nutrition knowledge was not associated with sleep disorder severity, body mass index or gender. The only significant difference detected was in relation to age, with older (=35 years) participants demonstrating greater knowledge in the 'food choices' domain compared with their younger counterparts (18-34 years; P < 0.030). Knowledge scores were similar to the general population. The findings suggest that nutrition knowledge alone is not an important target for weight control interventions for people with obstructive sleep apnea. However, given the complexities of sleep disorders, it may complement other strategies.

The drill-locate-drill (DLD) algorithm models the expert clinician's top-down diagnostic reasoning process, which generates a set of diagnostic hypotheses using a set of screening symptoms, and then tests them by eliciting specific clinical information for each differential diagnosis. The algorithm arrives at final diagnoses by matching the elicited clinical features with what is expected in each differential diagnosis using an efficient technique known as the orthogonal vector projection method. The DLD algorithm is compared with its rival select-test (ST) algorithm and its design/implementation in psychiatry, and evaluation using actual patient data is discussed.

Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.

Background: High rates of anxiety, depression and unmet needs are evident amongst haematological cancer patients undergoing treatment and their Support Persons. Psychosocial distress may be minimised by ensuring that patients are sufficiently involved in decision making, provided with tailored information and adequate preparation for potentially threatening procedures. To date, there are no published studies evaluating interventions designed to reduce psychosocial distress and unmet needs specifically in patients with haematological cancers and their Support Persons. This study will examine whether access to a web-based information tool and nurse-delivered telephone support reduces depression, anxiety and unmet information needs for haematological cancer patients and their Support Persons. Methods/Design: A non-blinded, parallel-group, multi-centre randomised controlled trial will be conducted to compare the effectiveness of a web-based information tool and nurse-delivered telephone support with usual care. Participants will be recruited from the haematology inpatient wards of five hospitals in New South Wales, Australia. Patients diagnosed with acute myeloid leukaemia, acute lymphoblastic leukaemia, Burkitt's lymphoma, Lymphoblastic lymphoma (B or T cell), or Diffuse Large B-Cell lymphoma and their Support Persons will be eligible to participate. Patients and their Support Persons will be randomised as dyads. Participants allocated to the intervention will receive access to a tailored web-based tool that provides accurate, up-to-date and personalised information about: cancer and its causes; treatment options including treatment procedures information; complementary and alternative medicine; and available support. Patients and Support Persons will complete self-report measures of anxiety, depression and unmet needs at 2, 4, 8 and 12 weeks post-recruitment. Patient and Support Person outcomes will be assessed independently. Discussion: This study will assess whether providing information and support using web-based and telephone support address the major psychosocial challenges faced by haematological patients and their Support Persons. The approach, if found to be effective, has potential to improve psychosocial outcomes for haematological and other cancer patients, reduce the complexity and burden of meeting patients' psychosocial needs for health care providers with high potential for translation into clinical practice.

Modifiable health risk behaviors such as smoking, overweight and obesity, risky alcohol consumption, physical inactivity, and poor nutrition contribute to a substantial proportion of the world's morbidity and mortality burden. General practitioners (GPs) play a key role in identifying and managing modifiable health risk behaviors. However, these are often underdetected and undermanaged in the primary care setting. We describe the potential of eHealth to help patients and GPs to overcome some of the barriers to managing health risk behaviors. In particular, we discuss (1) the role of eHealth in facilitating routine collection of patient-reported data on lifestyle risk factors, and (2) the role of eHealth in improving clinical management of identified risk factors through provision of tailored feedback, point-of-care reminders, tailored educational materials, and referral to online self-management programs. Strategies to harness the capacity of the eHealth medium, including the use of dynamic features and tailoring to help end users engage with, understand, and apply information need to be considered and maximized. Finally, the potential challenges in implementing eHealth solutions in the primary care setting are discussed. In conclusion, there is significant potential for innovative eHealth solutions to make a contribution to improving preventive care in the primary care setting. However, attention to issues such as data security and designing eHealth interfaces that maximize engagement from end users will be important to moving this field forward.

Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R2 increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.

Background: Stroke is a leading cause of death and disability internationally. One of the three effective interventions in the acute phase of stroke care is thrombolytic therapy with tissue plasminogen activator (tPA), if given within 4.5 hours of onset to appropriate cases of ischaemic stroke.Objectives: To test the effectiveness of a multi-component multidisciplinary collaborative approach compared to usual care as a strategy for increasing thrombolysis rates for all stroke patients at intervention hospitals, while maintaining accepted benchmarks for low rates of intracranial haemorrhage and high rates of functional outcomes for both groups at three months.Methods and design: A cluster randomised controlled trial of 20 hospitals across 3 Australian states with 2 groups: multi- component multidisciplinary collaborative intervention as the experimental group and usual care as the control group. The intervention is based on behavioural theory and analysis of the steps, roles and barriers relating to rapid assessment for thrombolysis eligibility; it involves a comprehensive range of strategies addressing individual-level and system-level change at each site. The primary outcome is the difference in tPA rates between the two groups post-intervention. The secondary outcome is the proportion of tPA treated patients in both groups with good functional outcomes (modified Rankin Score (mRS <2) and the proportion with intracranial haemorrhage (mRS =2), compared to international benchmarks.Discussion: TIPS will trial a comprehensive, multi-component and multidisciplinary collaborative approach to improving thrombolysis rates at multiple sites. The trial has the potential to identify methods for optimal care which can be implemented for stroke patients during the acute phase. Study findings will include barriers and solutions to effective thrombolysis implementation and trial outcomes will be published whether significant or not.Trial registration: Australian New Zealand Clinical Trials Registry: ACTRN12613000939796. © 2014 Paul et al.; licensee BioMed Central Ltd.

Schizophrenia is a highly heritable disorder. Genetic risk is conferred by a large number of alleles, including common alleles of small effect that might be detected by genome-wide association studies. Here we report a multi-stage schizophrenia genome-wide association study of up to 36,989 cases and 113,075 controls. We identify 128 independent associations spanning 108 conservatively defined loci that meet genome-wide significance, 83 of which have not been previously reported. Associations were enriched among genes expressed in brain, providing biological plausibility for the findings. Many findings have the potential to provide entirely new insights into aetiology, but associations at DRD2 and several genes involved in glutamatergic neurotransmission highlight molecules of known and potential therapeutic relevance to schizophrenia, and are consistent with leading pathophysiological hypotheses. Independent of genes expressed in brain, associations were enriched among genes expressed in tissues that have important roles in immunity, providing support for the speculated link between the immune system and schizophrenia. © 2014 Macmillan Publishers Limited. All rights reserved.

Background Schizophrenia is associated with lower pre-morbid intelligence (IQ) in addition to (pre-morbid) cognitive decline. Both schizophrenia and IQ are highly heritable traits. Therefore, we hypothesized that genetic variants associated with schizophrenia, including copy number variants (CNVs) and a polygenic schizophrenia (risk) score (PSS), may influence intelligence. Method IQ was estimated with the Wechsler Adult Intelligence Scale (WAIS). CNVs were determined from single nucleotide polymorphism (SNP) data using the QuantiSNP and PennCNV algorithms. For the PSS, odds ratios for genome-wide SNP data were calculated in a sample collected by the Psychiatric Genome-Wide Association Study (GWAS) Consortium (8690 schizophrenia patients and 11 831 controls). These were used to calculate individual PSSs in our independent sample of 350 schizophrenia patients and 322 healthy controls. Results Although significantly more genes were disrupted by deletions in schizophrenia patients compared to controls (p = 0.009), there was no effect of CNV measures on IQ. The PSS was associated with disease status (R 2 = 0.055, p = 2.1 × 10 -7) and with IQ in the entire sample (R 2 = 0.018, p = 0.0008) but the effect on IQ disappeared after correction for disease status. Conclusions Our data suggest that rare and common schizophrenia-associated variants do not explain the variation in IQ in healthy subjects or in schizophrenia patients. Thus, reductions in IQ in schizophrenia patients may be secondary to other processes related to schizophrenia risk. © Cambridge University Press 2013.