Evaluating the informatics for integrating biology and the bedside system for clinical research

<p>Abstract</p> <p>Background</p> <p>Selecting patient cohorts is a critical, iterative, and often time-consuming aspect of studies involving human subjects; informatics tools for helping streamline the process have been identified as important infrastructure components for enabling clinical and translational research. We describe the evaluation of a free and open source cohort selection tool from the Informatics for Integrating Biology and the Bedside (i2b2) group: the i2b2 hive.</p> <p>Methods</p> <p>Our evaluation included the usability and functionality of the i2b2 hive using several real world examples of research data requests received electronically at the University of Utah Health Sciences Center between 2006 - 2008. The hive server component and the visual query tool application were evaluated for their suitability as a cohort selection tool on the basis of the types of data elements requested, as well as the effort required to fulfill each research data request using the i2b2 hive alone.</p> <p>Results</p> <p>We found the i2b2 hive to be suitable for obtaining estimates of cohort sizes and generating research cohorts based on simple inclusion/exclusion criteria, which consisted of about 44% of the clinical research data requests sampled at our institution. Data requests that relied on post-coordinated clinical concepts, aggregate values of clinical findings, or temporal conditions in their inclusion/exclusion criteria could not be fulfilled using the i2b2 hive alone, and required one or more intermediate data steps in the form of pre- or post-processing, modifications to the hive metadata, etc.</p> <p>Conclusion</p> <p>The i2b2 hive was found to be a useful cohort-selection tool for fulfilling common types of requests for research data, and especially in the estimation of initial cohort sizes. For another institution that might want to use the i2b2 hive for clinical research, we recommend that the institution would need to have structured, coded clinical data and metadata available that can be transformed to fit the logical data models of the i2b2 hive, strategies for extracting relevant clinical data from source systems, and the ability to perform substantial pre- and post-processing of these data.</p

Lung dose threshold for interstitial pneumonitis for patients undergoing total body irradiation.

Purpose: To quantify dose response for lung irradiation and other predictors associated with IP (interstitial pneumonitis) for patients undergoing TBI (total body irradiation). Methods: A literature search on TBI identified 42 publications (including 3194 patients) that reported IP incidence rates along with lung doses, dose rates, dose fractionation schemes, and chemotherapy agents. Mean lung dose from these different fractionation regimens was converted to equivalent dose in 2 Gy fractions (EQD2) (a/b = 3 Gy). Data from fractionated and single fraction regimen subsets were analyzed separately and adjusted for incomplete lung tissue repair (half-life = 4 h) for fractionation schemes with \u3e1 fraction per day (EQD2-repair). Cox logistic regression was performed using multivariate analysis to identify predictors of IP. Dose response functions were generated using two different models and recursive partitioning was used to identify a lung dose threshold. Results: Cox logistic regression models found that EQD2, EQD2-repair, and chemotherapy agent cyclophosphamide (Cy) are significant predictors of IP. The models failed to deliver a reasonable dose response curve. Discrete data analysis of multi-fraction per day regimens identified a lung dose threshold of 7 Gy and 7.6 Gy for EQD2 and EQD2-repair, respectively, below which no IP toxicity was observed. Dose rate was not found to be an independent risk factor for IP. The most common chemotherapy agent was found to be Cy with 80% prevalence and mean dose of 120 mg/kg. Conclusion: Mean lung dose and Cy were identified as predictors of IP but the prediction model did not accurately reflect the IP rates of published data. Restricting lung EQD2 below the thresholds of 7.0 Gy and 7.6 Gy identified here for single and multiple fraction per day regimens, respectively, results in negligible IP incidence irrespective of dose rate. These lung doses are easily achievable with lung blocks or intensity modulation for standard 12 Gy prescription regimens