Software Tools for the Evaluation of Clinical Signs and Symptoms in the Medical Management of Acute Radiation Syndrome - A Five-year Experience
Port M, Haupt J, Ostheim P, Majewski M, Combs SE, Atkinson M, Abend M (2021)
Publication Type: Journal article
Publication year: 2021
Journal
Book Volume: 120
Pages Range: 400-409
Journal Issue: 4
DOI: 10.1097/HP.0000000000001353
Abstract
A suite of software tools has been developed for dose estimation (BAT, WinFRAT) and prediction of acute health effects (WinFRAT, H-Module) using clinical symptoms and/or changes in blood cell counts. We constructed a database of 191 ARS cases using the METREPOL (n = 167) and the SEARCH-database (n = 24). The cases ranged from unexposed (RC0), to mild (RC1), moderate (RC2), severe (RC3), and lethal ARS (RC4). From 2015-2019, radiobiology students and participants of two NATO meetings predicted clinical outcomes (RC, H-ARS, and hospitalization) based on clinical symptoms. We evaluated the prediction outcomes using the same input datasets with a total of 32 teams and 94 participants. We found that: (1) unexposed (RC0) and mildly exposed individuals (RC1) could not be discriminated; (2) the severity of RC2 and RC3 were systematically overestimated, but almost all lethal cases (RC4) were correctly predicted; (3) introducing a prior education component for non-physicians significantly increased the correct predictions of RC, ARS, and hospitalization by around 10% (p<0.005) with a threefold reduction in variance and a halving of the evaluation time per case; (4) correct outcome prediction was independent of the software tools used; and (5) comparing the dose estimates generated by the teams with H-ARS severity reflected known limitations of dose alone as a surrogate for H-ARS severity. We found inexperienced personnel can use software tools to make accurate diagnostic and treatment recommendations with up to 98% accuracy. Educational training improved the quality of decision making and enabled participants lacking a medical background to perform comparably to experts.
Involved external institutions
Universität Ulm
Germany (DE)
Technische Universität München (TUM)
Germany (DE)
Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich
Germany (DE)
Germany (DE)
Technische Universität München (TUM)
Germany (DE)
Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich
Germany (DE)
How to cite
APA:
Port, M., Haupt, J., Ostheim, P., Majewski, M., Combs, S.E., Atkinson, M., & Abend, M. (2021). Software Tools for the Evaluation of Clinical Signs and Symptoms in the Medical Management of Acute Radiation Syndrome - A Five-year Experience. Health Physics, 120(4), 400-409. https://doi.org/10.1097/HP.0000000000001353
MLA:
Port, Matthias, et al. "Software Tools for the Evaluation of Clinical Signs and Symptoms in the Medical Management of Acute Radiation Syndrome - A Five-year Experience." Health Physics 120.4 (2021): 400-409.
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