Pilot Medical Certification Period Health State Forecasts

DTFAWA-10-C-00080The Federal Aviation Administration (FAA) Office of Aerospace Medicine supports research to use available healthcare data to inform policies regarding pilots' medical certifications. The MITRE Corporation's Center for Advanced System Development (MITRE CAASD) was asked to examine methods in advanced data analytics and machine learning (ML) to inform such risk-based decision-making. As an initial step in assessing the potential predictive value of commercially available healthcare data, the FAA provided MITRE CAASD the IBM MarketScan dataset\u2014a large set of commercial healthcare claims records. Using this dataset, we developed methods for identifying health status and changes in health status across conditions; for measuring changes in health status among enrollees with diabetes mellitus (DM); and for measuring the onset of new cases of DM, traumatic brain injury, sleep apnea, and chronic obstructive pulmonary disease. We developed a repeatable workflow and modeled these conditions using a wide range of ML methods. We conclude that ML-based predictive modeling of health conditions from IBM MarketScan data is feasible and informative. However, additional clinical information from commercially available electronic health records would likely improve accuracy and more closely align with future FAA needs

Status of the VERITAS Observatory

VERITAS, an Imaging Atmospheric Cherenkov Telescope (IACT) system for gammma-ray astronomy in the GeV-TeV range, has recently completed its first season of observations with a full array of four telescopes. A number of astrophysical gamma-ray sources have been detected, both galactic and extragalactic, including sources previously unknown at TeV energies. We describe the status of the array and some highlight results, and assess the technical performance, sensitivity and shower reconstruction capabilities.Comment: Submitted to Proceedings of "4th Heidelberg International Symposium on High Energy Gamma-Ray Astronomy 2008

Veratridine produces distinct calcium response profiles in mouse Dorsal Root Ganglia neurons.

Nociceptors are a subpopulation of dorsal root ganglia (DRG) neurons that detect noxious stimuli and signal pain. Veratridine (VTD) is a voltage-gated sodium channel (VGSC) modifier that is used as an "agonist" in functional screens for VGSC blockers. However, there is very little information on VTD response profiles in DRG neurons and how they relate to neuronal subtypes. Here we characterised VTD-induced calcium responses in cultured mouse DRG neurons. Our data shows that the heterogeneity of VTD responses reflects distinct subpopulations of sensory neurons. About 70% of DRG neurons respond to 30-100 μM VTD. We classified VTD responses into four profiles based upon their response shape. VTD response profiles differed in their frequency of occurrence and correlated with neuronal size. Furthermore, VTD response profiles correlated with responses to the algesic markers capsaicin, AITC and α, β-methylene ATP. Since VTD response profiles integrate the action of several classes of ion channels and exchangers, they could act as functional "reporters" for the constellation of ion channels/exchangers expressed in each sensory neuron. Therefore our findings are relevant to studies and screens using VTD to activate DRG neurons

A Niche-Based Framework to Assess Current Monitoring of European Forest Birds and Guide Indicator Species' Selection

Concern that European forest biodiversity is depleted and declining has provoked widespread efforts to improve management practices. To gauge the success of these actions, appropriate monitoring of forest ecosystems is paramount. Multi-species indicators are frequently used to assess the state of biodiversity and its response to implemented management, but generally applicable and objective methodologies for species' selection are lacking. Here we use a niche-based approach, underpinned by coarse quantification of species' resource use, to objectively select species for inclusion in a pan-European forest bird indicator. We identify both the minimum number of species required to deliver full resource coverage and the most sensitive species' combination, and explore the trade-off between two key characteristics, sensitivity and redundancy, associated with indicators comprising different numbers of species. We compare our indicator to an existing forest bird indicator selected on the basis of expert opinion and show it is more representative of the wider community. We also present alternative indicators for regional and forest type specific monitoring and show that species' choice can have a significant impact on the indicator and consequent projections about the state of the biodiversity it represents. Furthermore, by comparing indicator sets drawn from currently monitored species and the full forest bird community, we identify gaps in the coverage of the current monitoring scheme. We believe that adopting this niche-based framework for species' selection supports the objective development of multi-species indicators and that it has good potential to be extended to a range of habitats and taxa

European Cystic Fibrosis Society standards of care: best practice guidelines

Specialised CF care has led to a dramatic improvement in survival in CF: in the last four decades, well above what was seen in the general population over the same period. With the implementation of newborn screening in many European countries, centres are increasingly caring for a cohort of patients who have minimal lung disease at diagnosis and therefore have the potential to enjoy an excellent quality of life and an even greater life expectancy than was seen previously. To allow high quality care to be delivered throughout Europe, a landmark document was published in 2005 that sets standards of care. Our current document builds on this work, setting standards for best practice in key aspects of CF care. The objective of our document is to give a broad overview of the standards expected for screening, diagnosis, pre-emptive treatment of lung disease, nutrition, complications, transplant/end of life care and psychological support. For comprehensive details of clinical care of CF, references to the most up to date European Consensus Statements, Guidelines or Position Papers are provided in Table 1. We hope that this best practice document will be useful to clinical teams both in countries where CF care is developing and those with established CF centres

Parallel evolution and adaptation to environmental factors in a marine flatfish: implications for fisheries and aquaculture management of the turbot (Scophthalmus maximus)

Unraveling adaptive genetic variation represents, in addition to the estimate of population demographic parameters, a cornerstone for the management of aquatic natural living resources, which, in turn, represent the raw material for breeding programs. The turbot (Scophthalmus maximus) is a marine flatfish of high commercial value living on the European continental shelf. While wild populations are declining, aquaculture is flourishing in southern Europe. We evaluated the genetic structure of turbot throughout its natural distribution range (672 individuals; 20 populations) by analyzing allele frequency data from 755 single nucleotide polymorphism discovered and genotyped by double‐digest RAD sequencing. The species was structured into four main regions: Baltic Sea, Atlantic Ocean, Adriatic Sea, and Black Sea, with subtle differentiation apparent at the distribution margins of the Atlantic region. Genetic diversity and effective population size estimates were highest in the Atlantic populations, the area of greatest occurrence, while turbot from other regions showed lower levels, reflecting geographical isolation and reduced abundance. Divergent selection was detected within and between the Atlantic Ocean and Baltic Sea regions, and also when comparing these two regions with the Black Sea. Evidence of parallel evolution was detected between the two low salinity regions, the Baltic and Black seas. Correlation between genetic and environmental variation indicated that temperature and salinity were probably the main environmental drivers of selection. Mining around the four genomic regions consistently inferred to be under selection identified candidate genes related to osmoregulation, growth, and resistance to diseases. The new insights are useful for the management of turbot fisheries and aquaculture by providing the baseline for evaluating the consequences of turbot releases from restocking and farming.Additional co-authors: Einar Eg Nielsen, The Aquatrace Consortium, and Paulino Martíne

ECFS best practice guidelines: the 2018 revision

Developments in managing CF continue to drive dramatic improvements in survival. As newborn screening rolls-out across Europe, CF centres are increasingly caring for cohorts of patients who have minimal lung disease on diagnosis. With the introduction of mutation-specific therapies and the prospect of truly personalised medicine, patients have the potential to enjoy good quality of life in adulthood with ever-increasing life expectancy. The landmark Standards of Care published in 2005 set out what high quality CF care is and how it can be delivered throughout Europe. This underwent a fundamental re-write in 2014, resulting in three documents; center framework, quality management and best practice guidelines. This document is a revision of the latter, updating standards for best practice in key aspects of CF care, in the context of a fast-moving and dynamic field. In continuing to give a broad overview of the standards expected for newborn screening, diagnosis, preventative treatment of lung disease, nutrition, complications, transplant/end of life care and psychological support, this consensus on best practice is expected to prove useful to clinical teams both in countries where CF care is developing and those with established CF centres. The document is an ECFS product and endorsed by the CF Network in ERN LUNG and CF Europe

Assessing Pilot Aeromedical Risk Analysis Using Healthcare Data [Structured Query Language (SQL) and Python Code]

693KA8-22-C-00001The Federal Aviation Administration (FAA) Office of Aerospace Medicine requires comprehensive longitudinal healthcare datasets to augment internal data for the purpose of conducting safety risk assessments to update medical standards (i.e. data driven, risk based decision making). The Federal Aviation Administration (FAA) tasked The MITRE Corporation\u2019s Center for Advanced Aviation System Development (MITRE CAASD), in its Innovation Partner role, to identify commercial healthcare datasets that hold potential value in forecasting medical risk and are suitable for integration into the Aeromedical Data Environment. MITRE CAASD performed a market survey of existing healthcare datasets available commercially or for public use. This market survey led to the identification of over 40 healthcare data sources, many of which contain numerous subordinate sets. An initial set of screening criteria ensured that candidate data sources were sufficiently suitable for modeling objectives; this screening reduced the set to three final candidate data sources. These three data sources were compared using a set of features relevant to risk modeling of aeromedically relevant outcomes by condition. This set of comparison features included their coverage of medical conditions of interest to the FAA, as well as factors impacting integration into the aeromedical data environment.The Federal Aviation Administration (FAA) Office of Aerospace Medicine is responsible for the medical certification of pilots such that the risk of pilot acute incapacitation is below a target risk threshold. This study sought to design a repeatable method of using commercial healthcare datasets to segment pilots with existing chronic conditions into acute incapacitation risk groups for the purpose of informing medical standards and certification policy guidance. Based on availability to the researchers, Merative\u2019s Explorys electronic health record dataset, comprising 11 years of data, was used for method development. In collaboration with FAA medical officers, researchers operationalized pilot acute incapacitation as a composite outcome of 16 medical conditions and their associated diagnostic codes. These conditions were identified based on the scenario that a pilot is medically qualified to fly, conducts an adequate preflight self-assessment, and during flight experiences the acute onset of a state incompatible with active aircraft control such that orderly transfer of control to another pilot or automation is unlikely. Approaches to developing quantitative risk models for the outcome of pilot acute incapacitation were explored for four chronic conditions: diabetes, obstructive sleep apnea, chronic obstructive pulmonary disease, and atrial fibrillation. Three general approaches were explored: whole population risk, disease severity models, and a de novo method. Using whole-population risk resulted in over- and - underestimation of pilot acute incapacitation risk for a significant portion of the population. Using existing disease severity scores produced poor risk stratification for pilot acute incapacitation. The de novo method was designed to be broadly applicable to any condition of interest. The method was comprised of the following steps: (1) define the cohort for the condition of interest; (2) use a clinical reference tool (DynaMed, UpToDate, etc.) to produce relevant clinical factors; (3) use a clinical mapping tool (e.g., Unified Medical Language System) to link clinical factors to medical codes; (4) use information gain to select risk factors (relevant to both the chronic condition of interest and the outcome) from clinical factors for inclusion in pilot acute incapacitation risk models; (5) compute stratified incidence rates for pilot acute incapacitation; and (5) compare incident rates to the target risk threshold.The code repository described below contains the SQL and python code for utilizing the Explorys EHR dataset to1. Compute stratified incidence rates for aeromedically relevant conditions2. Construct machine learning models to predict changes in risk categorie