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The Department of Veterans Affairs (VA) Cooperative Studies Program (CSP), Clinical Research Pharmacy Coordinating Center (Center) has supported clinical trials for more than four decades. Managing information from clinical trials and published results in the Big Data era presents new challenges and opportunities. These include and are not limited to data attribution, aggregation, adaptability, and prompt analysis. Hence, the Center has created a dynamic application to present a broad understanding of the clinical trials’ achievements. To collect crucial information from clinical trials, this application includes 1) data attribution to identify provenance and to preserve relationships between trials and resulting publications, 2) data normalization to deal with variety of formats and concepts, 3) data aggregation to integrate information from different trials, and 4) data analysis with a friendly interface to consult aggregated information promptly. This work establishes a Semantic Data Model for each clinical trial to create a summary of key information in a machine-readable format, and to enrich each summary with semantic information. In addition, it allows the union of these models to represent a global knowledge source from a set of clinical trials. The organized models offer compatibility and interoperability within and among clinical trials.

Computed tomography perfusion provides information on tissue viability according to proposed thresholds. We evaluated thresholds for ischemic core and tissue at risk and subsequently tested their accuracy in independent datasets.Tissue at risk was evaluated in patients with persistent arterial occlusions, and ischemic core thresholds in patients with recanalization and major clinical improvement. Scans were randomly allocated to derivation or validation groups for tissue at risk and core analysis. Optimum thresholds using mean transit time (MTT), cerebral blood flow (CBF), cerebral blood volume, and delay time (DT) were assessed.Absolute MTT, relative MTT and DT were best derived predictors of tissue at risk with thresholds of ≥ 7 seconds, ≥ 125%, and ≥ 2 seconds respectively. DT ≥ 2 seconds was the best predictor in the validation dataset (95% agreement levels = -44 to +30 mL, Bias = -6.9). Absolute and relative MTT were the best derived predictors of infarct volume in the core group (8 seconds and 125% respectively) but relative CBF of ≤ 45% performed best in the core validation dataset.Time-based perfusion thresholds perform well as predictors of tissue at risk of infarction with DT the best predictor. Relative CBF was the best predictor of ischemic core. Evaluation in larger populations is needed to confirm the performance of tissue viability thresholds.