OHDSI MEETINGS THIS WEEK
Patient-level prediction (Eastern hemisphere) workgroup meeting - Wednesday at 3pm Hong Kong time
Population-level estimation (Western hemisphere) workgroup - Wednesday at 12pm ET
NLP working group meeting - Wednesday at 2pm ET
Dial +1 (571) 317-3122 (United States)
Enter conference ID: 707-196-421
Screen Sharing: https://global.gotomeeting.com/join/707196421
Hadoop WG meeting - Friday at 11am ET
WebEx: http://cloudera.webex.com/meet/sdolley ANNOUNCEMENTS
2017 OHDSI Symposium - The date and location for the 2017 OHDSI Symposium has been confirmed! This year’s symposium will take place on Wednesday, October 18th at the Bethesda North Marriott. Full-day tutorial sessions will take place on October 19-20th. If you’re interested in attending, please save the date.
Symposium Registration - Registration for the symposium will open shortly. Check out the symposium website for updates: https://www.ohdsi.org/events/2017-ohdsi-symposium/
Call for Sponsorship - To fund the 2017 symposium we need to raise $200K to cover operating costs of venue rental, lunch, audio/visuals and recordings fees. We’re reaching out to the entire community for sponsors who want to support this important event and allow us to build upon the success of past symposia. More details: OHDSI Symposium 2017 - call for sponsorship
In the minds of geniuses we find our own neglected thoughts. COMMUNITY PUBLICATIONS
Streamlining cardiovascular clinical trials to improve efficiency and generalisability
Personalized glucose forecasting for type 2 diabetes using data assimilation.
DJ Albers, M Levine, B Gluckman, H Ginsberg, G Hripcsak and L Mamykina,
PLoS computational biology, 2017 04
Type 2 diabetes leads to premature death and reduced quality of life for 8% of Americans. Nutrition management is critical to maintaining glycemic control, yet it is difficult to achieve due to the high individual differences in glycemic response to nutrition. Anticipating glycemic impact of different meals can be challenging not only for individuals with diabetes, but also for expert diabetes educators. Personalized computational models that can accurately forecast an impact of a given meal on an individual's blood glucose levels can serve as the engine for a new generation of decision support tools for individuals with diabetes. However, to be useful in practice, these computational engines need to generate accurate forecasts based on limited datasets consistent with typical self-monitoring practices of individuals with type 2 diabetes. This paper uses three forecasting machines: (i) data assimilation, a technique borrowed from atmospheric physics and engineering that uses Bayesian modeling to infuse data with human knowledge represented in a mechanistic model, to generate real-time, personalized, adaptable glucose forecasts; (ii) model averaging of data assimilation output; and (iii) dynamical Gaussian process model regression. The proposed data assimilation machine, the primary focus of the paper, uses a modified dual unscented Kalman filter to estimate states and parameters, personalizing the mechanistic models. Model selection is used to make a personalized model selection for the individual and their measurement characteristics. The data assimilation forecasts are empirically evaluated against actual postprandial glucose measurements captured by individuals with type 2 diabetes, and against predictions generated by experienced diabetes educators after reviewing a set of historical nutritional records and glucose measurements for the same individual. The evaluation suggests that the data assimilation forecasts compare well with specific glucose measurements and match or exceed in accuracy expert forecasts. We conclude by examining ways to present predictions as forecast-derived range quantities and evaluate the comparative advantages of these ranges.
Inter-labeler and intra-labeler variability of condition severity classification models using active and passive learning methods
Measuring and Improving the Quality of Data Used for Syndromic Surveillance
Quality adjusted life year gains associated with administration of recombinant tissue-type plasminogen activator for treatment of acute ischemic stroke: 1998-2011.
DP Lubeck, MD Danese, J Duryea, M Halperin, D Tayama, E Yu, D Lalla and JC Grotta,
International journal of stroke : official journal of the International Stroke Society, Feb 2016
Intravenous recombinant tissue-type plasminogen activator (r-tPA) is an approved treatment for select patients with acute ischemic stroke (AIS). Data indicate r-tPA improves functional outcome three months after AIS compared with placebo. This study models the increase in quality adjusted life years (QALYs) associated with r-tPA compared with similar patients not treated with r-tPA.Hospital discharge data for AIS and r-tPA were obtained from the Nationwide Inpatient Sample from 1998 to 2011. Discharge location (home, rehabilitation, long-term care, death) was mapped to modified Rankin Scale (mRS) scores based on National Institute of Neurological Disorders and Stroke (NINDS) Study Group Part 1 and 2 clinical studies. The mRS scores were mapped to relative risk of death and QALYs obtained from the literature. The model estimated expected survival and QALYs by age, gender and mRS for patients receiving r-tPA. Life expectancy and QALYs for patients not receiving r-tPA were estimated based on discharge location and mRS for placebo patients in the NINDS study.AIS discharges declined from over 635,000 in 1998 to over 593,000 in 2011. A total of 183,235 patients received r-tPA. Utilization of r-tPA increased from 1% of AIS patients in 1998 to over 4% in 2011. Estimated projections for QALYs gained from utilization of r-tPA to QALYS without r-tPA were just under 240,000 for the 13 years and with no discounting, and just over 165,000 assuming 3% annual discounting. In the most conservative scenario, assuming no difference in proportional discharge status (i.e. patients not treated with r-tPA are discharged in the same manner as r-tPA patients), the estimated life years gained are approximately 35,000 and QALYS gained are approximately 90,000.r-tPA for AIS has resulted in estimated gains in quality-adjusted life years due to reduction in disability and improvement in functioning since its introduction in 1998.