- Poster presentation
- Open Access
Predicting the response to therapy from a mathematical model
© BioMed Central Ltd. 2004
- Published: 15 March 2004
- Statistical Model
- Tissue Factor
- Host Response
- Bacterial Load
To determine the feasibility and usefulness of computer simulations in evaluating therapeutic strategies and patient selection in clinical trials of sepsis.
We simulated an interventional trial of a neutralizing body against tissue necrosis factor (anti-TNF) in sepsis based on a mechanistic mathematical model that includes a bacterial infection, the host response, and a therapeutic intervention. Simulated cases differed by bacterial load and virulence, as well as individual propensity to mount and modulate an inflammatory response. We submitted 1000 cases to three doses and durations of anti-TNF, and present the results of the simulation. To evaluate the usefulness of modeling to improve patient selection, we constructed a logistic model with a four-valued outcome: (1) helped by treatment, (2) survives irrespective of treatment, (3) dies irrespective of treatment, and (4) harmed by treatment. Independent predictors were 'measured' at the time of disease detection and 60 min later, and included serum TNF, IL-10, IL-6, activated protein C, thrombin, tissue factor, blood pressure and cell counts. All results from the statistical model are reported in a validation cohort.
Our models points out how to improve patient selection and how therapy could be individualized.