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caBIG(TM) Workspaces
Project List
The BMIF team is assisting Drs. Davidson and DeMichele (Division of Hematology-Oncology) to develop a database for a Clinical Trial of Lapatinib in Breast Cancer. The first phase of design will concentrate on patient scheduling and sample tracking. The scheduling module will track a patient progress from date of consent until date of surgery. This module will facilitate two main goals:
- Ensure all appointments are correctly scheduled within narrowly-defined time frames
- Monitor a patient status to alert the research team if an appointment is missed or if a patient becomes ineligible for the trial. The sample-tracking module uses a well-defined syntax to automatically assign semantically meaningful sample IDs. This will reduce the potential for mislabeling of samples, determine location of all existing samples for a patient, and annotate any anomalies or deviations from standard protocols. Future modules will be developed to store genomic and other data.
The Case Report Forms (CRFs) will be captured using Oracle Clinical. Oracle Clinical is a database system for capturing, storing, and processing clinical trial data. Our team will work with Dr. Landis and his colleagues in the Clinical Research Computing Unit to utilize standards that have been developed within caBIG and CDISC projects in the design and implementation of thi.s database. Using a clinical database and electronic data capture in this project will decrease development time and increase cost efficiency, while still maintaining high data and security standards required for clinical trials.
The cancer Biomedical Informatics Grid, or caBIG(TM) , is a voluntary network or grid connecting individuals and institutions to enable the sharing of data and tools, creating a World Wide Web of cancer research. The goal is to speed the delivery of innovative approaches for the prevention and treatment of cancer. The infrastructure and tools created by caBIG(TM) also have broad utility outside the cancer community. caBIG(TM) is being developed under the leadership of the National Cancer Institute's Center for Bioinformatics.
The Biomedical Informatics Facility are currently participating in seven caBIG(TM) Workspaces. These workspaces include: Clinical Trial Management Systems Workspace, Data Sharing and Intellectual Capital Workspace, Documentation and Training Workspace, Integrative Cancer Research Workspace, In Vivo Imaging Workspace, Tissue Banks and Pathology Tools Workspace and Strategic Planning Workspace.
Through the Center for Population Health and Health Disparities (CPHHD) initiative, the center at University of Pennsylvania
has begun to address significant gaps in our knowledge about factors that
predict prostate cancer outcomes, and in particular the causes of disparity in prostate cancer outcomes
between men of African and Caucasian descent.
The Biomedical Informatics Facility is involved as a Core facility for the CPHHD. The BMIF team provides integrated data management and model-building for the
multivariate analysis of biological, behavioral, social, and environmental factors on prostate cancer
outcomes.
The DSMC SAE database is being developed with Vicki Sallée,
Administrative Director, and will be used to capture all Serious Adverse
Events (on-site and off) that are reported to and by Abramson Cancer
Center investigators. This database will be used by the DSMC for
evaluating events, identifying safety trends/patterns, making
recommendations for changes to consent forms, evaluating ACC
participation in various arms of protocols and for determining whether
or not a study should be closed due to safety concerns. This database
is not used for reporting Serious Adverse Events.
The BMIF has created the web site ephra.org to serve as an
informational overview for the EPHRA organization. Further, the BMIF
has provided a secure online blog site to facilitate communication
between EPHRA members. In addition to these communication services,
the BMIF plans to provide a web application to allow EPHRA members to
share de-identified patient data among centers which will enable EPHRA
members to more easily find appropriate study subjects.
The aim of the preterm birth network is to accelerate the pace of premature birth research by focusing on global genomic and proteomic strategies and the dissemination of genomic and proteomic data to the scientific community. Specifically, the network will:
- Design and implement hypothesis-driven, mechanistic studies based on large-scale, high-output genomic and proteomic approaches, and
- Provide a public, web-based, genomic and proteomic database for data mining and data deposition by the research community.
The University of Pennsylvania is the Analytical Core component of the Network for Premature Birth Research. UPenn will use caTISSUE Core to track all tissue collected and sent to Penn for analysis. BMIF team members will assist with data analysis and training on caTISSUE tools.
The Health Services Research Data Center (HSRDC) is designed to
address the growing data acquisition, storage, and processing needs of
health services researchers at the University of Pennsylvania. It is
collaboratively produced and maintained by the Leonard Davis Institute
of Health Economics (LDI) and the Biomedical Informatics Core Facility
(BMIF). The Data Center is a collection of data, computing, and
storage resources with the following aims:
- Facilitating the acquisition, use, and sharing of data.
- Providing universally secure storage, thereby eliminating system
duplicity and security inconsistencies.
- Assisting with large administrative data files, including tape
reading, file conversion, and guidance about linking Medicare files.
The BMIF is providing and will continue to provide technological
assistance to Tom Cappola, heart failure specialist at the University
of Pennsylvania Hospital, with an ongoing heart failure study. In
addition to creating a web application to capture, store, and query
clinical survey data, the BMIF has also recommended scannable form
technology and provided data cleanup and normalization services.
Future plans include linking the survey data to other clinical data in
order to allow robust query and report generation.
The BMIF team is assisting Dr. Michael Ming (Dermatology-Oncology) in
building an application that will be used to document and track of all of their
subjects that are seen by the Pigment Lesion Group (PLG). The scope of the project is to:
- Convert and normalize the current database from Paradox to Oracle. A more secure and stable platform for the data and bring the database up to compliance with University standards.
- Build a web based application that will interface with the Oracle database. This will permit the PLG to access the data from any computer with internet access. The application will also allow the PLG to facilitate the sharing of data with other oncologist. This application will have numerous security features and audit trail capability.
- Build robust query and report tools to help increasing efficiency.
- Link database with IDX to receive automatic feeds for patient scheduling. Allowing the PLG to be better informed and productivity plan resources.
The Sepsis application is being developed as an extension to the Hematological Malignancy Patient Database. The lead investigators on the project (Drs. Fuchs, Luger, and Mato) are examining the ability of a laboratory test to predict severe sepsis in hematologic malignancy patients with a high clinical suspicion of infection. Initial data for a patient is captured at the time of consent. A field within patient demographics allows a new record to be designated as a Leukemia patient, a Sepsis patient, or both. After a new patient has been enrolled, three sets of vitals and a suite of laboratory data will be collected each day. The laboratory data is automatically captured by parsing the results of the HL7 feed from the William Pepper Laboratory of the Hospital of the University of Pennsylvania. Data pertaining to particular events (e.g., blood culture, infection, or ICU transfer) are also stored for a patient.
The BMIF is creating an automated system to assist with the daily operation of
the Toxicology Clinical Lab. Using the Cerner and OTTR systems, they retrieve
data about patients and prepare reports containing past Mycophenolic Acid (MPA)
results and other relevant clinical information. Based on this history and the
current MPA result, a user-defined comment is prepared that is reported together
with the result to help clinicians make decision about either keeping or
changing the dose of MPA.
Currently, we are automating the data retrieval, report generation, preparation
of the comment, and reporting the result. This system will be expanded to allow
for the mining of the collected data with statistical analysis tools.
Through the Transdisciplinary Tobacco Use Research Centers (TTURC) initiative, the center at the University of Pennsylvania has begun to address the gap in knowledge regarding treatment options for
smoking cessation.
The Biomedical Informatics Facility serves as a Core facility and enhances the current and past TTURC data management infrastructure. This is accomplished by incorporating the data repository within a distributed computing environment that will facilitate data integration, cross-project data analysis and collaboration. This centralized strategy will increase efficiency and reduce the cost of undertaking the proposed research projects. In addition the inclusion of biostatistical support provided by the Penn Biostatistics Unit will optimize research design and data analysis in a coordinated manner.
University of Pennsylvania Hematological Malignancy Patient Database
The BMIF team is collaborating with Drs. Carroll, Loren, and Luger in the Division of Hematology-Oncology to develop a Leukemia Patient database.The timeline for a patient begins with diagnosis and includes data pertaining to their disease, including molecular, chromosomal, and immunophenotypic characteristics, as well as enrollment in clinical trials, and treatments (which may include chemotherapy and/or transplants).This tool will aid in finding appropriate individuals for future clinical trials, supervised and non-supervised data mining, and the integration of genotype and phenotype data to enable translational research.
