Risk prediction models based on hematological/body parameters for chemotherapy-induced adverse effects in Chinese colorectal cancer patients

Purpose To determine risk factors and develop novel prediction models for chemotherapy-induced adverse effects (CIAEs) in Chinese colorectal cancer (CRC) patients receiving capecitabine. Methods A total of 233 Chinese CRC patients receiving post-operative chemotherapy with capecitabine were randomly divided into a training set (70%) and a validation set (30%). CIAE-related hematological/body parameters were screened by univariate logistic regression. Based on a set of factors selected from LASSO (least absolute shrinkage and selection operator) logistic regression, stepwise multivariate logistic regression was applied to develop prediction models. Area under the receiver operating characteristic (ROC) curve and Hosmer-Lemeshow (HL) test were used to evaluate the discriminatory ability and the goodness of fit of each model. Results In total, 35 variables were identified to be associated with CIAEs in univariate analysis. Developed multivariable models had AUCs (area under curve) ranging from 0.625 to 0.888 and 0.428 to 0.760 in the training and validation set, respectively. The grade >= 1 anemia multivariable model achieved the best discriminatory ability with AUC of 0.760 (95%CI: 0.609-0.912) and good calibration with HL P value of 0.450. Then, a nomogram was constructed to predict grade >= 1 anemia, which included variables of age, pre-operative hemoglobin count, and pre-operative albumin count, with C-indexes of 0.775 and 0.806 in the training and validation set, respectively. Conclusions This study identified valuable hematological/body parameters related to CIAEs. A nomogram based on the multivariable model including three hematological/body predictors can accurately predict grade >= 1 anemia, facilitating clinicians to implement personalized medicine early for Chinese CRC patients receiving post-operative chemotherapy for better safety treatment.