P22. Prognostic Predictive Model for the Development of Osteoarthritis using Electronic Medical Record Data

Background: As the most common joint disorder worldwide (1), osteoarthritis represents a growing concern for older adults. Prognostic predictive models (statistical models used to predict future disease development (2)) may enable the identification of patients at high risk of developing osteoarthritis, allowing for health and lifestyle modifications aimed at reducing the risk of disease development (3,4). Methods: For our project, we accessed the DELPHI (Deliver Primary Healthcare Information) database which contains de-identified electronic medical records of more than 60,000 primary care patients in Ontario (5,6). From these data, we constructed a retrospective cohort examining patients’ risk factors and followed them over time to observe incident cases of osteoarthritis. This retrospective cohort was used to develop and test prognostic predictive models, using methods such as logistic regression, to determine the models’ ability to predict development of osteoarthritis. Models were evaluated, examining both discrimination (AUC) and calibration (calibration plots), using a reserved portion of patient data. Results: A logistic regression model was built that predicts the incidence of osteoarthritis based on patient age, sex, Body Mass Index (BMI), osteoporosis status, and leg injury status (AUC: 0.73). Discussion & Conclusion: By creating a prognostic predictive model for osteoarthritis, we aim to support primary health care practitioners in estimating an individual patient’s risk of osteoarthritis; thereby allowing practitioners and patients to create unique plans to address the patient’s personal risk factors. Interdisciplinary Reflection: This project is highly interdisciplinary as it spans the fields of epidemiology, statistics, health informatics, primary health care, and computer science. References: 1. Lopez AD, Mathers CD, Ezzati M, Jamison DT, Murray CJL. Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data. Lancet (London, England) [Internet]. 2006 May 27 [cited 2016 Feb 13];367(9524):1747–57. Available from: http://www.ncbi.nlm.nih.gov/pubmed/16731270 2. Hendriksen JMT, Geersing GJ, Moons KGM, de Groot JAH. Diagnostic and prognostic prediction models. J Thromb Haemost [Internet]. 2013 Jun [cited 2016 Aug 10];11 Suppl 1:129–41. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23809117 3. Felson DT, Zhang Y, Anthony JM, Naimark A, Anderson JJ. Weight loss reduces the risk for symptomatic knee osteoarthritis in women. The Framingham Study. Ann Intern Med [Internet]. 1992 Apr 1 [cited 2016 Jun 23];116(7):535–9. Available from: http://www.ncbi.nlm.nih.gov/pubmed/1543306 4. Felson DT. Weight and osteoarthritis. Am J Clin Nutr [Internet]. 1996 Mar [cited 2016 Jun 23];63(3 Suppl):430S–432S. Available from: http://www.ncbi.nlm.nih.gov/pubmed/8615335 5. CPCSSN. DELPHI (Deliver Primary Healthcare Information) Project [Internet]. 2013. Available from: http://cpcssn.ca/regional-networks/delphi-deliver-primary-healthcare-information-project/ 6. Birtwhistle R, Keshavjee K, Lambert-Lanning A, Godwin M, Greiver M, Manca D, et al. Building a pan-Canadian primary care sentinel surveillance network: initial development and moving forward. J Am Board Fam Med [Internet]. 2009 Jan [cited 2016 May 19];22(4):412–22. Available from: http://www.ncbi.nlm.nih.gov/pubmed/1958725

Prognostic Predictive Model to Estimate the Risk of Multiple Chronic Diseases: Constructing Copulas Using Electronic Medical Record Data

Introduction: Multimorbidity, the presence of two or more chronic diseases in an individual, is a pressing medical condition. Novel prevention methods are required to reduce the incidence of multimorbidity. Prognostic predictive models estimate a patient’s risk of developing chronic disease. This thesis developed a single predictive model for three diseases associated with multimorbidity: diabetes, hypertension, and osteoarthritis. Methods: Univariate logistic regression models were constructed, followed by an analysis of the dependence that existed using copulas. All analyses were based on data from the Canadian Primary Care Sentinel Surveillance Network. Results: All univariate models were highly predictive, as demonstrated by their discrimination and calibration. Copula models revealed the dependence between each disease pair. Discussion: By estimating the risk of multiple chronic diseases, prognostic predictive models may enable the prevention of chronic disease through identification of high-risk individuals or delivery of individualized risk assessments to inform patient and health care provider decision-making

Development and Validation of a Computational Model for Predicting the Behavior of Plumes from Large Solid Rocket Motors

Exhaust plumes from large solid rocket motors fired at ATK's Promontory test site carry particulates to high altitudes and typically produce deposits that fall on regions downwind of the test area. As populations and communities near the test facility grow, ATK has become increasingly concerned about the impact of motor testing on those surrounding communities. To assess the potential impact of motor testing on the community and to identify feasible mitigation strategies, it is essential to have a tool capable of predicting plume behavior downrange of the test stand. A software package, called PlumeTracker, has been developed and validated at ATK for this purpose. The code is a point model that offers a time-dependent, physics-based description of plume transport and precipitation. The code can utilize either measured or forecasted weather data to generate plume predictions. Next-Generation Radar (NEXRAD) data and field observations from twenty-three historical motor test fires at Promontory were collected to test the predictive capability of PlumeTracker. Model predictions for plume trajectories and deposition fields were found to correlate well with the collected dataset

A burst chasing x-ray polarimeter

Gamma-ray bursts are one of the most powerful explosions in the universe and have been detected out to distances of almost 13 billion light years. The exact origin of these energetic explosions is still unknown but the resulting huge release of energy is thought to create a highly relativistic jet of material and a power-law distribution of electrons. There are several theories describing the origin of the prompt GRB emission that currently cannot be distinguished. Measurements of the linear polarization would provide unique and important constraints on the mechanisms thought to drive these powerful explosions. We present the design of a sensitive, and extremely versatile gamma-ray burst polarimeter. The instrument is a photoelectric polarimeter based on a time-projection chamber. The photoelectric time-projection technique combines high sensitivity with broad band-pass and is potentially the most powerful method between 2 and 100 keV where the photoelectric effect is the dominant interaction process. We present measurements of polarized and unpolarized X-rays obtained with a prototype detector and describe the two mission concepts; the Gamma-Ray Burst Polarimeter (GRBP) for the U.S. Naval Academy satellite MidSTAR-2, and the Low Energy Polarimeter (LEP) onboard POET, a broadband polarimetry concept for a small explorer mission

GRB Polarimetry with POET

POET (Polarimeters for Energetic Transients) represents a concept for a Small Explorer (SMEX) satellite mission, whose principal scientific goal is to understand the structure of GRB sources through sensitive X‐ray and γ‐ray polarization measurements. The payload consists of two wide field‐of‐view (FoV) instruments: a Low Energy Polarimeter (LEP) capable of polarization measurements in the energy range from 2–15 keV and a high energy polarimeter (Gamma‐Ray Polarimeter Experiment or GRAPE) that would measure polarization in the 60–500 keV energy range. The POET spacecraft provides a zenith‐pointed platform for maximizing the exposure to deep space. Spacecraft rotation provides a means of effectively dealing with any residual systematic effects in the polarization response. POET provides sufficient sensitivity and sky coverage to measure statistically significant polarization (for polarization levels in excess of 20%) for ∼80 GRBs in a two‐year mission. High energy polarization data would also be obtained for SGRs, solar flares, pulsars and other sources of astronomical interest

Integration of MODIS land and atmosphere products with a coupled-process model to estimate gross primary productivity and evapotranspiration from 1 km to global scales

We propose the Breathing Earth System Simulator (BESS), an upscaling approach to quantify global gross primary productivity and evapotranspiration using MODIS with a spatial resolution of 1-5 km and a temporal resolution of 8 days. This effort is novel because it is the first system that harmonizes and utilizes MODIS Atmosphere and Land products on the same projection and spatial resolution over the global land. This enabled us to use the MODIS Atmosphere products to calculate atmospheric radiative transfer for visual and near infrared radiation wave bands. Then we coupled atmospheric and canopy radiative transfer processes, with models that computed leaf photosynthesis, stomatal conductance and transpiration on the sunlit and shaded portions of the vegetation and soil. At the annual time step, the mass and energy fluxes derived from BESS showed strong linear relations with measurements of solar irradiance (r(2) = 0.95, relative bias: 8%), gross primary productivity (r(2) = 0.86, relative bias: 5%) and evapotranspiration (r(2) = 0.86, relative bias: 15%) in data from 33 flux towers that cover seven plant functional types across arctic to tropical climatic zones. A sensitivity analysis revealed that the gross primary productivity and evapotranspiration computed in BESS were most sensitive to leaf area index and solar irradiance, respectively. We quantified the mean global terrestrial estimates of gross primary productivity and evapotranpiration between 2001 and 2003 as 118 +/- 26 PgC yr(-1) and 500 +/- 104 mm yr(-1) (equivalent to 63,000 +/- 13,100 km(3) yr(-1)), respectively. BESS-derived gross primary productivity and evapotranspiration estimates were consistent with the estimates from independent machine-learning, data-driven products, but the process-oriented structure has the advantage of diagnosing sensitivity of mechanisms. The process-based BESS is able to offer gridded biophysical variables everywhere from local to the total global land scales with an 8-day interval over multiple years.Keywords: Energy fluxes, Water vapor exchange, Foliage clumping index, Carbon dioxide, Stomal conductance, Net ecosystem exchange, Sitchensis bong carr, Radiative transfer, Oak hickory forest, Leaf area inde