A New Smartphone Application to Predict Hematologic Acute Radiation Syndrome Based on Blood Cell Count Changes—The H-module App. Issue 1 (July 2020)

• Record Type: Journal Article
• Authors: Majewski, Matthäus; Rozgic, Marco; Ostheim, Patrick; Port, Matthias; Abend, Michael
• Is Part Of: Health physics. Volume 119:Issue 1(2020)
• Publication Date: 2020-07
• Subjects: accidents, nuclear -- blood -- health effects -- radiation effects; Biophysics -- Periodicals; Health Physics -- periodicals; Radiation Protection -- periodicals; Radiotherapy -- periodicals; Medische fysica; Electronic journals; 612.01448
• DOI: 10.1097/HP.0000000000001247
• Languages: English
• ISSNs: 0017-9078

Abstract

Abstract : Abstract: Treatment regimens for acute radiation syndrome have been improved over the past years. The application of appropriate therapy relies on rapid and high-throughput tests ideally conducted in the first 3 d after a radiation exposure event. We have examined the utility of blood cell counts (BCCs) 3 d post irradiation to predict clinical outcome for hematologic acute radiation syndrome (HARS). The BCCs and HARS severity information originated from data available in the System-for-Evaluation-and-Archiving-of-Radiation Accidents-based-on-Case-Histories (SEARCH). We found an almost complete discrimination of unexposed (HARS score H0) vs. irradiated individuals during model development and validation (negative predictive value > 94%) when using BCC data for all 3 d. We also found that BCC data increased the correct prediction of exposed individuals from day 1 to day 3. We developed spreadsheets to calculate the likelihood of correct diagnoses of the worried-well, requirement of hospitalization (HARS 2-4), or development of severe hematopoietic syndrome (HARS 3-4). In two table-top exercises, we found the spreadsheets were confusing and cumbersome, so we converted the spreadsheets into a smartphone application, named the H-module App, designed for ease of use, wider dissemination, and accommodation of co-morbidities in the HARS severity prediction algorithm.