RDI - HIV Resistance Response Database Initiative
RDI AIDS HIV Resistance
 
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The database
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Bioinformatics


The database is at the very heart of what the RDI does. It is the engine that drives us forward to a better understanding of how different factors affect the susceptibility of the virus and the way patients respond to different drugs, and towards more informed, individualized treatment decisions.

The database contains thousands of data samples from numerous sources around the world. As of January 2010, the database contained data from approximately 60,000 patients.

Each data sample consists of a treatment change episode (TCE). A TCE includes all the relevant data for a patient around when s/he were started on a new course of antiretroviral drug therapy. These data include the following critical elements:

  • The plasma viral load (amount of HIV in the bloodstream) immediately before the treatment change (baseline)
  • The genotype at baseline
  • The CD4 count at baseline
  • Treatment history information
  • The drugs used in the new regimen
  • The viral load at a subsequent time point (between 4 and 48 weeks but typically 12, 16 or 24 weeks) so that the change in viral load (the virological response) can be calculated

The graph below illustrates the data that constitute a TCE.

TCE Diagram


It is vital to the success of the RDI that we obtain more data so that we can develop the database further. If you have data to contribute please contact us as soon as possible. An information sheet on our data requirements is available to be downloaded as a pdf below.

Data Requirements (133k)


Database structure

The RDI database has been conceptually modeled into a 'star/snowflake schema' as used in the design of data marts and data warehouses. This modeling approach allows for good performance when querying the database and is suited for data that is read only or data that is periodically updated.

The RDI has been modeled on an Oracle 11g database. The Oracle database is an industry standard database and one that is well known for its stability, scalability and performance.


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