Destination Area: Data and Decisions (D&D)
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The DA Data and Decisions advances the human condition and society with better decisions through data. D&D integrates all DAs and SGAs with data analytics and decision sciences. Work in this area embraces equity in the human condition by seeking the equitable distribution and availability of physical safety and well-being, psychological well-being, respect for human dignity, and access to crucial material and social resources throughout the world’s diverse communities. D&D also addresses policymaking and policy analysis, collaborating at the intersection of scientific evidence, governance, and analyses to translate scholarship into practice.
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Browsing Destination Area: Data and Decisions (D&D) by Department "Biomedical Engineering and Mechanics"
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- Association of Blood Biomarkers With Acute Sport-Related Concussion in Collegiate Athletes: Findings From the NCAA and Department of Defense CARE ConsortiumMcCrea, Michael A.; Broglio, Steven P.; McAllister, Thomas W.; Gill, Jessica M.; Giza, Christopher C.; Huber, Daniel L.; Harezlak, Jaroslaw; Cameron, Kenneth L.; Houston, Megan N.; McGinty, Gerald T.; Jackson, Jonathan C.; Guskiewicz, Kevin M.; Mihalik, Jason P.; Brooks, M. Alison; Duma, Stefan M.; Rowson, Steven; Nelson, Lindsay D.; Pasquina, Paul; Meier, Timothy B.; Foroud, Tatiana; Katz, Barry P.; Saykin, Andrew J.; Campbell, Darren E.; Svoboda, Steven J.; Goldman, Joshua T.; DiFiori, John P. (2020-01-24)Question Is sport-related concussion associated with levels of traumatic brain injury biomarkers in collegiate athletes? Findings In this case-control study of 504 collegiate athletes with concussion, contact sport control athletes, and non-contact sport athletes, the athletes with concussion had significant elevations in multiple traumatic brain injury biomarkers compared with preseason baseline and with 2 groups of control athletes without concussion during the acute postinjury period. Meaning These results suggest that blood biomarkers can be used as research tools to inform the underlying pathophysiological mechanism of concussion and provide additional support for future studies to optimize and validate biomarkers for potential clinical use in sport-related concussion. This case-control study examines the association between sport-related concussion and levels of traumatic brain injury biomarkers in collegiate athletes. Importance There is potential scientific and clinical value in validation of objective biomarkers for sport-related concussion (SRC). Objective To investigate the association of acute-phase blood biomarker levels with SRC in collegiate athletes. Design, Setting, and Participants This multicenter, prospective, case-control study was conducted by the National Collegiate Athletic Association (NCAA) and the US Department of Defense Concussion Assessment, Research, and Education (CARE) Consortium from February 20, 2015, to May 31, 2018, at 6 CARE Advanced Research Core sites. A total of 504 collegiate athletes with concussion, contact sport control athletes, and non-contact sport control athletes completed clinical testing and blood collection at preseason baseline, the acute postinjury period, 24 to 48 hours after injury, the point of reporting being asymptomatic, and 7 days after return to play. Data analysis was conducted from March 1 to November 30, 2019. Main Outcomes and Measures Glial fibrillary acidic protein (GFAP), ubiquitin C-terminal hydrolase-L1 (UCH-L1), neurofilament light chain, and tau were quantified using the Quanterix Simoa multiplex assay. Clinical outcome measures included the Sport Concussion Assessment Tool-Third Edition (SCAT-3) symptom evaluation, Standardized Assessment of Concussion, Balance Error Scoring System, and Brief Symptom Inventory 18. Results A total of 264 athletes with concussion (mean [SD] age, 19.08 [1.24] years; 211 [79.9%] male), 138 contact sport controls (mean [SD] age, 19.03 [1.27] years; 107 [77.5%] male), and 102 non-contact sport controls (mean [SD] age, 19.39 [1.25] years; 82 [80.4%] male) were included in the study. Athletes with concussion had significant elevation in GFAP (mean difference, 0.430 pg/mL; 95% CI, 0.339-0.521 pg/mL; P < .001), UCH-L1 (mean difference, 0.449 pg/mL; 95% CI, 0.167-0.732 pg/mL; P < .001), and tau levels (mean difference, 0.221 pg/mL; 95% CI, 0.046-0.396 pg/mL; P = .004) at the acute postinjury time point compared with preseason baseline. Longitudinally, a significant interaction (group x visit) was found for GFAP (F-7,F-1507.36 = 16.18, P < .001), UCH-L1 (F-7,F-1153.09 = 5.71, P < .001), and tau (F-7,F-1480.55 = 6.81, P < .001); the interaction for neurofilament light chain was not significant (F-7,F-1506.90 = 1.33, P = .23). The area under the curve for the combination of GFAP and UCH-L1 in differentiating athletes with concussion from contact sport controls at the acute postinjury period was 0.71 (95% CI, 0.64-0.78; P < .001); the acute postinjury area under the curve for all 4 biomarkers combined was 0.72 (95% CI, 0.65-0.79; P < .001). Beyond SCAT-3 symptom score, GFAP at the acute postinjury time point was associated with the classification of athletes with concussion from contact controls (beta = 12.298; 95% CI, 2.776-54.481; P = .001) and non-contact sport controls (beta = 5.438; 95% CI, 1.676-17.645; P = .005). Athletes with concussion with loss of consciousness or posttraumatic amnesia had significantly higher levels of GFAP than athletes with concussion with neither loss of consciousness nor posttraumatic amnesia at the acute postinjury time point (mean difference, 0.583 pg/mL; 95% CI, 0.369-0.797 pg/mL; P < .001). Conclusions and Relevance The results suggest that blood biomarkers can be used as research tools to inform the underlying pathophysiological mechanism of concussion and provide additional support for future studies to optimize and validate biomarkers for potential clinical use in SRC.
- Cultivating Emerging & Black Swan TechnologiesMahajan, Roop L. (2012-09-15)
- Decision-adjusted driver risk predictive models using kinematics informationMao, Huiying; Guo, Feng; Deng, Xinwei; Doerzaph, Zachary R. (Elsevier, 2021-06)Accurate prediction of driving risk is challenging due to the rarity of crashes and individual driver heterogeneity. One promising direction of tackling this challenge is to take advantage of telematics data, increasingly available from connected vehicle technology, to obtain dense risk predictors. In this work, we propose a decision-adjusted framework to develop optimal driver risk prediction models using telematics-based driving behavior information. We apply the proposed framework to identify the optimal threshold values for elevated longitudinal acceleration (ACC), deceleration (DEC), lateral acceleration (LAT), and other model parameters for predicting driver risk. The Second Strategic Highway Research Program (SHRP 2) naturalistic driving data were used with the decision rule of identifying the top 1% to 20% of the riskiest drivers. The results show that the decision-adjusted model improves prediction precision by 6.3% to 26.1% compared to a baseline model using non-telematics predictors. The proposed model is superior to models based on a receiver operating characteristic curve criterion, with 5.3% and 31.8% improvement in prediction precision. The results confirm that the optimal thresholds for ACC, DEC and LAT are sensitive to the decision rules, especially when predicting a small percentage of high-risk drivers. This study demonstrates the value of kinematic driving behavior in crash risk prediction and the necessity for a systematic approach for extracting prediction features. The proposed method can benefit broad applications, including fleet safety management, use-based insurance, driver behavior intervention, as well as connected-vehicle safety technology development.