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

Association of Smooth Muscle Myosin and its Carboxyl Isoforms with Actin Isoforms in Aorta Smooth Muscle

The contraction mechanism of smooth muscle is not fully understood. The primary interaction that leads to the formation of tension, the myosin-actin crossbridge, has been studied extensively. However, even this aspect of the contraction has proven not to be as simple as it might seem. There are several isoforms of smooth muscle myosin and actin, and the differences in the activities of these isoforms and their interactions during the contractile process are largely unknown. The studies to be discussed are directed at the determination of the interaction of these isoforms during the contraction of rat aortic smooth muscle. Chapter II describes the association of smooth muscle myosin with two of the actin isoforms found in smooth muscle, α-actin and β-actin, using a novel method of fluorescence resonance energy transfer (FRET) to examine this association in both the A7r5 cell model and in intact tissue. We show that the contractile apparatus undergoes significant remodeling during contraction and that the interaction of myosin with α-actin and β-actin is different at the various time points of contraction. In Chapter III, we describe more detailed experiments examining the two different myosin tail isoforms, SM1 and SM2. The results of these studies confirm our findings of remodeling of the cytoskeleton and the contractile apparatus during contraction and show that α-actin and β-actin interact differently with these myosin isoforms. The results provide the first direct evidence of contractile remodeling in smooth muscle and suggest that complex changes in actin-myosin interaction may be important in the contraction of this muscle type

Measuring Drinking Peer Caretaking: Toward Informing Peer-Based Alcohol Interventions

The purpose of the study was to pilot test a measure of a construct defined as Drinking Peer Caretaking (DPC). Most alcohol use among college students occurs in social situations among peer groups (Baer, 2002; Perkins, 2002b). However, understanding the dynamics of peer groups needs more attention since empirical information in this area is currently lacking. A broader understanding of caretaking behaviors within college student drinking peer groups could serve as a basis for developing peer-facilitated interventions. Principal Components Analysis (PCA) suggested a two factor solution (proactive and reactive caretaking). Following PCA, tests of internal consistency reliability (Cronbach’s alpha), and validity (convergent, concurrent, predictive, and discriminant) were conducted, and group differences were assessed based on gender, class standing, place of residence, and race/ethnicity. The measure showed high reliability and modest validity. Gender differences were found on proactive and reactive caretaking, such that women were higher than men on both. First year students scored higher on proactive caretaking than seniors did. No other group differences emerged. DPC appears to be a viable construct with useful implications for researchers and prevention professionals. Further study is needed to confirm the factor structure and continue validation of the measure

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

The Human Dimensions of Waterfowl Hunters at Cheyenne Bottoms Wildlife Area, Barton County, Kansas

An on-site human dimension survey was applied at Cheyenne Bottoms Wildlife Area (CHBW), Kansas, to evaluate waterfowl hunters’ support for three alternative management strategies. The strategies included in the survey were: 1) the creation of a refuge-in-time where hunting would be allowed for the entire day, but only on odd-numbered calendar dates, 2) the designation of an existing pool as a primitive pool, i.e., no motorized watercraft allowed, and 3) the creation of a refuge-in-time where hunting would only be allowed in a given pool from ½ hour before sunrise to 1300 hours, but hunting would be allowed every day during that time. Waterfowl hunters at CHBW were surveyed during three different season frameworks during the 2007-2008 and 2008-2009 waterfowl seasons: September teal season, early duck season, and late duck and goose season. There were no significant differences detected relative to season framework; however, waterfowl hunters at CHBW did support the implementation of a primitive pool. The analyses of these surveys will be used to help direct future management decisions, in an effort to increase waterfowl hunter participation and satisfaction at CHBW

Strategies to Overcome Network Congestion in Infrastructure Systems

Networked Infrastructure systems deliver services and/or products from point to point along the network. They include transportation networks (e.g., rails, highways, airports, sea ports), telecommunication networks (by frequency-bounded airwaves or cables), and utilities (e.g., electric power, water, gas, oil, sewage). Each is a fixed capacity system having marked time-of-day and day-of-week patterns of demand. Usually, the statistics of demand, including hourly patterns (i.e., means and variances) are well known and often correlated with outside factors such as weather (short term) and the general economy (longer term)

Estimating the Benefits of Electric Vehicle Smart Charging at Non-Residential Locations: A Data-Driven Approach

In this paper, we use data collected from over 2000 non-residential electric vehicle supply equipments (EVSEs) located in Northern California for the year of 2013 to estimate the potential benefits of smart electric vehicle (EV) charging. We develop a smart charging framework to identify the benefits of non-residential EV charging to the load aggregators and the distribution grid. Using this extensive dataset, we aim to improve upon past studies focusing on the benefits of smart EV charging by relaxing the assumptions made in these studies regarding: (i) driving patterns, driver behavior and driver types; (ii) the scalability of a limited number of simulated vehicles to represent different load aggregation points in the power system with different customer characteristics; and (iii) the charging profile of EVs. First, we study the benefits of EV aggregations behind-the-meter, where a time-of-use pricing schema is used to understand the benefits to the owner when EV aggregations shift load from high cost periods to lower cost periods. For the year of 2013, we show a reduction of up to 24.8% in the monthly bill is possible. Then, following a similar aggregation strategy, we show that EV aggregations decrease their contribution to the system peak load by approximately 40% when charging is controlled within arrival and departure times. Our results also show that it could be expected to shift approximately 0.25kWh (~2.8%) of energy per non-residential EV charging session from peak periods (12PM-6PM) to off-peak periods (after 6PM) in Northern California for the year of 2013.Comment: Pre-print, under review at Applied Energ