Application of multivariate modeling for radiation injury assessment: A proof of concept (Q2262593)

Summary: Multivariate radiation injury estimation algorithms were formulated for estimating severe hematopoietic acute radiation syndrome (H-ARS) injury (i.e., response category three or RC3) in a rhesus monkey total-body irradiation (TBI) model. Classical CBC and serum chemistry blood parameters were examined prior to irradiation (d 0) and on d 7, 10, 14, 21, and 25 after irradiation involving 24 nonhuman primates (NHP) (\textit{Macaca mulatta}) given 6.5-Gy \(^{60}\)Co \(\Upsilon \)-rays (0.4 Gy \(min^{ - 1}\)) TBI. A correlation matrix was formulated with the RC3 severity level designated as the ``dependent variable'' and independent variables down selected based on their radioresponsiveness and relatively low multicollinearity using stepwise-linear regression analyses. Final candidate independent variables included CBC counts (absolute number of neutrophils, lymphocytes, and platelets) in formulating the ``CBC'' RC3 estimation algorithm. Additionally, the formulation of a diagnostic CBC and serum chemistry ``CBC-SCHEM'' RC3 algorithm expanded upon the CBC algorithm model with the addition of hematocrit and the serum enzyme levels of aspartate aminotransferase, creatine kinase, and lactate dehydrogenase. Both algorithms estimated RC3 with over 90\% predictive power. Only the CBC-SCHEM RC3 algorithm, however, met the critical three assumptions of linear least squares demonstrating slightly greater precision for radiation injury estimation, but with significantly decreased prediction error indicating increased statistical robustness.