There are continuous concerns about the possibility of nuclear and radiological terrorism or accidents that may result in mass casualties in densely populated areas. To guide medical personnel in their clinical decisions for effective medical management and treatment of exposed individuals, biomarkers are usually applied to examine radiation-induced biological changes to assess the severity of radiation injury. Among these, peripheral blood cell counts are traditionally regarded as the most practical and rapid diagnostic indicators.
Previous algorithms required early data points of blood cell counts. While the earliest dose estimation of the patient provides the most important information to assist triage management and treatment decisions, in many cases, the victims were not identified until several days or even weeks after exposure. This can happen in large-scale accidents when medical resources are limited and/or the management system is overwhelmed.
The HemoDose tools are built upon solid physiological and pathophysiological understanding of mammalian hematopoietic systems, and rigorous coarse-grained bio-mathematical modeling and validation. Using single or serial counts of granulocyte, lymphocyte, leukocyte, or platelet after exposure, these tools can estimate absorbed doses of adult victims very rapidly and accurately. Patient data in some historical accidents are utilized as examples to demonstrate the capabilities of these tools as a rapid point-of-care diagnostic or centralized high-throughput assay system in a large-scale radiological disaster scenario. Unlike previous dose prediction algorithms, the HemoDose tools establish robust correlations between the absorbed doses and victims’ various types of blood cell counts not only in the early time window (1 or 2 days), but also in the late phase (up to 4 weeks) after exposure.
There are four modules in HemoDose: granulocyte count, lymphocyte count, leukocyte count, and platelet count. Each module contains three functions: Model the Clinical Data, Plot the Historical Data, and Model the Historical Data. To model the clinical data, the user can either choose a prepared data file or input data manually, and run the simulation to get an estimated dose with a plot. To plot the historical data, the user can pick a data set from the list and click the plot button. Similarly, to model the historical data, the user can pick a data set from a list of historical events and run the simulation to get an estimated dose, which can be compared with the recorded dose of the patient.
The software can be downloaded for Windows desktop or run directly on the Web.