A quantitative assessment model of T-cell immune function for predicting risks of infection and rejection during the early stage after liver transplantation

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Although more and more clinical studies indicated that ImmuKnow assay could efficiently assess the immune status of recipients, it still has the challenge to predict the occurrence of clinical adverse events. This study aimed to establish a quantitative assessment model, which could more efficiently predict immune function of T lymphocytes after liver transplantation based on three indexes: CD4+ T lymphocyte count (C), CD4+/CD8+ ratio (R), and ImmuKnow adenosine triphosphate (ATP) value (A). We selected 194 recipients and measured the A, C, and R index every week, then obtained the Fisher linear discriminant functions by SPSS 16.0. Next, we divided the recipients into three groups: infection, stable, and rejection groups according to clinical status. After calculating, the discriminant function, 0.012A + 0.019C + 1.322R (simplified into T = 2A + 3C + 200R), was selected to represent the T-cell-mediated immune function. Based on the model, the optimal cutoff T values for infection and rejection were 1415 (sensitivity = 80%, specificity = 79.9%,AUC = 92.3%) and 1939.5 (sensitivity = 93.9%, specificity = 77.6%, AUC = 88.6%), relatively (p < 0.001). In conclusion, this model may be a more feasible way to evaluate the cellular immune function status in liver transplantation recipients.

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