Early-phase dose and H-ARS severity-based response category algorithms using hematological biomarkers in baboon gamma and mixed-field neutron/gamma radiation models.

IF 2.4
David L Bolduc, Lalitha Kurada, Francis Hérodin, William F Blakely
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Abstract

Introduction: The purpose of this study was to determine early phase CBC markers that could predict radiation dose and hematopoietic acute radiation syndrome (H-ARS) severity in a male baboon radiation model, and to determine the translational relevancy of these findings derived with known radiation doses as it pertains to the human algorithms based on reconstructed doses.

Methods: A panel of hematologic biomarkers was measured from blood-cell samples collected at 0 to 5d after receiving 0-8 Gy of TBI exposures of gamma or mixed field (neutron/gamma) using a standard blood cell counter. The database was used in an initial stepwise multiple-regression model for estimating radiation dose using data from 1-3d. A logistic-regression model-fitting approach was also applied for estimating H-ARS severity using TBI and PBI radiation data consisting of gamma only or mixed field collected from 1-5d.

Results: The dose estimation model consisted of the variable's lymphocytes-natural log day 3 (p < .001) and platelets-squared day 3 (p < .0001), which yielded an estimation accuracy of fit of R2 = 0.6760 (p < .001) and accuracy ∼75%. The logistic regression H-ARS severity algorithm consisted of lymphocytes (p < .001) and platelets-natural log (p < .0001) with overall accuracy of 93.4 (0 vs 2.5 & 2 H-ARS), 96.6 (0 vs 2.5 H-ARS), and 99.3 (0 vs 3 H-ARS) %. The predicted H-ARS severity outcome for the 0-5-day models showed area under the curves of 0.974, 0.990, 0.995 respectively by ROC curve analysis.

Discussion: The resultant study supports the proof-of-concept that initial dose estimation and H-ARS severity-response-category algorithms using standard blood-cell parameters can provide both rapid triage and radiation injury assessment following gamma and mixed (neutron/gamma)-field exposures.

Conclusion: These findings, based on a baboon radiation model, provide confirmatory evidence of the utility of hematological biomarkers predicting radiation dose and injury severity.

在狒狒伽玛和混合场中子/伽玛辐射模型中使用血液学生物标志物的早期剂量和基于H-ARS严重程度的反应分类算法。
本研究的目的是确定可以预测雄性狒狒辐射模型中辐射剂量和造血急性辐射综合征(H-ARS)严重程度的早期CBC标记物,并确定这些发现与已知辐射剂量的转化相关性,因为它与基于重建剂量的人类算法有关。方法:在接受0-8 Gy的伽玛或混合场(中子/伽玛)TBI暴露后0至5d收集血细胞样本,使用标准血细胞计数器测量一组血液学生物标志物。该数据库用于初始逐步多元回归模型,利用1-3d数据估计辐射剂量。采用logistic回归模型拟合方法,利用1-5d收集的TBI和PBI辐射数据(仅伽马或混合场)估计H-ARS的严重程度。结果:剂量估计模型由变量的淋巴细胞-自然对数第3天(p < 0.001)和血小板-平方第3天(p < 0.001)组成,其拟合估计精度为R2 = 0.6760 (p < 0.001),准确度为75%。逻辑回归H-ARS严重程度算法由淋巴细胞(p < 0.001)和血小板-自然对数(p < 0.001)组成,总体准确率为93.4 (0 vs 2.5和2 H-ARS), 96.6 (0 vs 2.5 H-ARS)和99.3 (0 vs 3 H-ARS) %。0 ~ 5 d模型预测H-ARS严重程度的曲线下面积分别为0.974、0.990、0.995。讨论:由此产生的研究支持概念验证,即使用标准血细胞参数的初始剂量估计和H-ARS严重反应分类算法可以提供伽玛和混合(中子/伽玛)场暴露后的快速分类和辐射损伤评估。结论:这些基于狒狒辐射模型的发现,为血液学生物标志物预测辐射剂量和损伤严重程度的效用提供了确凿的证据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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