Rapid Prediction of Hematologic Acute Radiation Syndrome in Radiation Injury Patients Using Peripheral Blood Cell Counts

Abstract

Rapid clinical triage of radiation injury patients is essential for determining appropriate diagnostic and therapeutic interventions. We examined the utility of blood cell counts (BCCs) in the first three days postirradiation to predict clinical outcome, specifically for hematologic acute radiation syndrome (HARS). We analyzed BCC test samples from radiation accident victims (n = 135) along with their clinical outcome HARS severity scores (H1-4) using the System for Evaluation and Archiving of Radiation Accidents based on Case Histories (SEARCH) database. Data from nonirradiated individuals (H0, n = 132) were collected from an outpatient facility. We created binary categories for severity scores, i.e., 1 (H0 vs. H1-4), 2 (H0-1 vs. H2-4) and 3 (H0-2 vs. H3-4), to assess the discrimination ability of BCCs using unconditional logistic regression analysis. The test sample contained 454 BCCs from 267 individuals. We validated the discrimination ability on a second independent group comprised of 275 BCCs from 252 individuals originating from SEARCH (HARS 1-4), an outpatient facility (H0) and hospitals (e.g., leukemia patients, H4). Individuals with a score of H0 were easily separated from exposed individuals based on developing lymphopenia and granulocytosis. The separation of H0 and H1-4 became more prominent with increasing hematologic severity scores and time. On day 1, lymphocyte counts were most predictive for discriminating binary categories, followed by granulocytes and thrombocytes. For days 2 and 3, an almost complete separation was achieved when BCCs from different days were combined, supporting the measurement of sequential BCC. We found an almost complete discrimination of H0 vs. irradiated individuals during model validation (negative predictive value, NPV > 94%) for all three days, while the correct prediction of exposed individuals increased from day 1 (positive predictive value, PPV 78-89%) to day 3 (PPV > 90%). The models were unable to provide predictions for 10.9% of the test samples, because the PPVs or NPVs did not reach a 95% likelihood defined as the lower limit for a prediction. We developed a prediction model spreadsheet to provide early and prompt diagnostic predictions and therapeutic recommendations including identification of the worried well, requirement of hospitalization or development of severe hematopoietic syndrome. These results improve the provisional classification of HARS. For the final diagnosis, further procedures (sequential diagnosis, retrospective dosimetry, clinical follow-up, etc.) must be taken into account. Clinical outcome of radiation injury patients can be rapidly predicted within the first three days postirradiation using peripheral BCC.

The first in vivo multiparametric comparison of different radiation exposure biomarkers in human blood

“…In case of a large-scale radiological event, the subsequent medical management would strongly rely on triage of potentially exposed individuals. This can be achieved by assessing clinical signs and/or symptoms indicating severe exposure [ 21 ], however, procedures for determination of low doses are not fully developed and current biodosimetry suffers from certain gaps in the methodology [ 22 ] in the terms of sensitivity, limited high-throughput capacity etc. In this study, we wanted to compare different biomarkers of radiation exposure.…”

“…CDKN1A up-regulation is significantly maintained throughout the treatment in both sets of patients and, although this was following 25 fractions of radiotherapy, it could potentially be useful for follow-up/retrospective biodosimetry. However, the expression of CDKN1A has also been associated with normal tissue sensitivity to RT [ 21 ] and its response has been variable in ex vivo irradiated blood samples [ 37 ].…”

Analysis of mRNA Expression Patterns in Peripheral Blood Cells of 3 Patients With Cancer After the First Fraction of 2 Gy Irradiation: An Integrated Case Report and Systematic Review

“…11 Port et al used blood cell counts as a biomarker for clinical radiation injury and suggested that these counts could be used as a rapid predictor of such injury in the first 3 days after radiation exposure. 12 Ye et al evaluated the prognostic value of the following predictors in patients with nasopharyngeal carcinoma treated with RT: absolute neutrophil count (ANC), absolute platelet count, absolute lymphocyte count (ALC), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR). This analysis indicated that NLR and PLR were useful and cost-effective predictors that could be applied to inform more precise treatment strategies.…”

The New Zealand white rabbit animal model of acute radiation syndrome: hematopoietic and coagulation-based parameters by radiation dose following supportive care

“…Non-survivors demonstrated the highest rebound granulocytosis on day 1, >5.2 Â 10 3 /lL, and the greatest depletion on day 3, < 0.1 Â 10 3 /lL; such large swings in cell counts observed in the first 3 days postradiation, identify a critical time period of large pathophysiologic changes warranting additional study. Pinpointing the first 72 h post-radiation as a time interval of significant interest and predictive value has recently been reported using human data analytics, in which blood count data demonstrated >78% positive predictive value (PPV) on day 1 and >90% PPV on day 3 for the development of severe acute radiation syndrome (Port et al 2017). These findings suggest that the dramatic changes in ANC observed in the first 72 h may be used to predict mortality In review of human data from irradiated individuals, platelet depletion at LD50-100 exposures of 15-25 (Â10 9 /L) was first observed within 7 days post-radiation (Mettler 2007).…”