Dynamic predictive probabilities to monitor rapid cystic fibrosis disease progression.

Cystic fibrosis (CF) is a progressive, genetic disease characterized by frequent, prolonged drops in lung function. Accurately predicting rapid underlying lung-function decline is essential for clinical decision support and timely intervention. Determining whether an individual is experiencing a period of rapid decline is complicated due to its heterogeneous timing and extent, and error component of the measured lung function. We construct individualized predictive probabilities for "nowcasting" rapid decline. We assume each patient's true longitudinal lung function, S(t), follows a nonlinear, nonstationary stochastic process, and accommodate between-patient heterogeneity through random effects. Corresponding lung-function decline at time t is defined as the rate of change, S'(t). We predict S'(t) conditional on observed covariate and measurement history by modeling a measured lung function as a noisy version of S(t). The method is applied to data on 30 879 US CF Registry patients. Results are contrasted with a currently employed decision rule using single-center data on 212 individuals. Rapid decline is identified earlier using predictive probabilities than the center's currently employed decision rule (mean difference: 0.65 years; 95% confidence interval (CI): 0.41, 0.89). We constructed a bootstrapping algorithm to obtain CIs for predictive probabilities. We illustrate real-time implementation with R Shiny. Predictive accuracy is investigated using empirical simulations, which suggest this approach more accurately detects peak decline, compared with a uniform threshold of rapid decline. Median area under the ROC curve estimates (Q1-Q3) were 0.817 (0.814-0.822) and 0.745 (0.741-0.747), respectively, implying reasonable accuracy for both. This article demonstrates how individualized rate of change estimates can be coupled with probabilistic predictive inference and implementation for a useful medical-monitoring approach

Gaps and opportunities in refractory status epilepticus research in children: A multi-center approach by the Pediatric Status Epilepticus Research Group (pSERG)

PURPOSE: Status epilepticus (SE) is a life-threatening condition that can be refractory to initial treatment. Randomized controlled studies to guide treatment choices, especially beyond first-line drugs, are not available. This report summarizes the evidence that guides the management of refractory convulsive SE (RCSE) in children, defines gaps in our clinical knowledge and describes the development and works of the \u27pediatric Status Epilepticus Research Group\u27 (pSERG). METHODS: A literature review was performed to evaluate current gaps in the pediatric SE and RCSE literature. In person and online meetings helped to develop and expand the pSERG network. RESULTS: The care of pediatric RCSE is largely based on extrapolations of limited evidence derived from adult literature and supplemented with case reports and case series in children. No comparative effectiveness trials have been performed in the pediatric population. Gaps in knowledge include risk factors for SE, biomarkers of SE and RCSE, second- and third-line treatment options, and long-term outcome. CONCLUSION: The care of children with RCSE is based on limited evidence. In order to address these knowledge gaps, the multicenter pSERG was established to facilitate prospective collection, analysis, and sharing of de-identified data and biological specimens from children with RCSE. These data will allow identification of treatment strategies associated with better outcomes and delineate evidence-based interventions to improve the care of children with SE

Constructing a semantic predication gold standard from the biomedical literature

<p>Abstract</p> <p>Background</p> <p>Semantic relations increasingly underpin biomedical text mining and knowledge discovery applications. The success of such practical applications crucially depends on the quality of extracted relations, which can be assessed against a gold standard reference. Most such references in biomedical text mining focus on narrow subdomains and adopt different semantic representations, rendering them difficult to use for benchmarking independently developed relation extraction systems. In this article, we present a multi-phase gold standard annotation study, in which we annotated 500 sentences randomly selected from MEDLINE abstracts on a wide range of biomedical topics with 1371 semantic predications. The UMLS Metathesaurus served as the main source for conceptual information and the UMLS Semantic Network for relational information. We measured interannotator agreement and analyzed the annotations closely to identify some of the challenges in annotating biomedical text with relations based on an ontology or a terminology.</p> <p>Results</p> <p>We obtain fair to moderate interannotator agreement in the practice phase (0.378-0.475). With improved guidelines and additional semantic equivalence criteria, the agreement increases by 12% (0.415 to 0.536) in the main annotation phase. In addition, we find that agreement increases to 0.688 when the agreement calculation is limited to those predications that are based only on the explicitly provided UMLS concepts and relations.</p> <p>Conclusions</p> <p>While interannotator agreement in the practice phase confirms that conceptual annotation is a challenging task, the increasing agreement in the main annotation phase points out that an acceptable level of agreement can be achieved in multiple iterations, by setting stricter guidelines and establishing semantic equivalence criteria. Mapping text to ontological concepts emerges as the main challenge in conceptual annotation. Annotating predications involving biomolecular entities and processes is particularly challenging. While the resulting gold standard is mainly intended to serve as a test collection for our semantic interpreter, we believe that the lessons learned are applicable generally.</p

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe

Clustering semantic spaces of suicide notes and newsgroups articles

Historically, suicide risk assessment has relied on question-and-answer type tools. These tools, built on psychometric advances, are widely used because of availability. Yet there is no known tool based on biologic and cognitive evidence. This absence often cause a vexing clinical problem for clinicians who question the value of the result as time passes. The purpose of this paper is to describe one experiment in a series of experiments to develop a tool that combines Biological Markers (Bm) with Thought Markers (Tm), and use machine learning to compute a real-time index for assessing the likelihood repeated suicide attempt in the next six-months. For this study we focus using unsupervised machine learning to distinguish between actual suicide notes and newsgroups. This is important because it gives us insight into how well these methods discriminate between real notes and general conversation

96IGT-136&apos; PIV MEASUREMENTS OF PERIODICALLY FORCED FLAT PLATE FILM COOLING FLOWS WITH HIGH FREE STREAM TURBULENCE

ABSTRACT Two color double pulsed Particle Image Velocimetry (PIV) measurements of simulated turbine film cooling flows have been made for blowing ratios of 0.5, 0.7, and 1.0 in the near field of the film cooling hole, x/ds2.5. The effect of the vane wake on the rotor film cooling flow is simulated by periodically forcing the film cooling flows at the non dimensional reduced frequency. Phase locked measurements at 45 deg. increments of the periodic film forcing (0, 45, 90, 135, 180, 225, 270, and 315 deg.) for free stream turbulence levels of 1 and 17% have been made. The effects of reduced frequencies of 20 and 80, at free stream turbulence levels of 1 and 17% on the spreading of the film cooling jet are investigated. Increases in the jet spread with forcing and free stream turbulence are &gt; 2 times those in the unforced 1% free stream turbulence case

Detection and Visualization of Compositionally Similar cis-Regulatory Element Clusters in Orthologous and Coordinately Controlled Genes

Evolutionarily conserved noncoding genomic sequences represent a potentially rich source for the discovery of gene regulatory regions. However, detecting and visualizing compositionally similar cis-element clusters in the context of conserved sequences is challenging. We have explored potential solutions and developed an algorithm and visualization method that combines the results of conserved sequence analyses (BLASTZ) with those of transcription factor binding site analyses (MatInspector) (http://trafac.chmcc.org). We define hits as the density of co-occurring cis-element transcription factor (TF)-binding sites measured within a 200-bp moving average window through phylogenetically conserved regions. The results are depicted as a Regulogram, in which the hit count is plotted as a function of position within each of the two genomic regions of the aligned orthologs. Within a high-scoring region, the relative arrangement of shared cis-elements within compositionally similar TF-binding site clusters is depicted in a Trafacgram. On the basis of analyses of several training data sets, the approach also allows for the detection of similarities in composition and relative arrangement of cis-element clusters within nonorthologous genes, promoters, and enhancers that exhibit coordinate regulatory properties. Known functional regulatory regions of nonorthologous and less-conserved orthologous genes frequently showed cis-element shuffling, demonstrating that compositional similarity can be more sensitive than sequence similarity. These results show that combining sequence similarity with cis-element compositional similarity provides a powerful aid for the identification of potential control regions