Predicting early post-chemotherapy adverse events in patients with hematological malignancies: a retrospective study

The purpose of this study was to develop a mathematical model that predicts the definite adverse events following chemotherapy in patients with hematological malignancies (HMs).

This is a retrospective cohort study including 1157 cases with HMs. Firstly, we screened and verified the independent risk factors associated with post-chemotherapy adverse events by both univariate and multivariate logistic regression analysis using 70 % of randomly selected cases (training set). Secondly, we proposed a mathematical model based on those selected factors. The calibration and discrimination of the model were assessed by Hosmer-Lemeshow (H-L) test and area under the receiver operating characteristic (ROC) curve, respectively. Lastly, the predicative power of this model was further tested in the remaining 30 % of cases (validation set).

Our statistical analysis indicated that liver dysfunction (OR = 2.164), active infection (OR = 3.619), coagulation abnormalities (OR = 4.614), intensity of chemotherapy (OR = 10.001), acute leukemia (OR = 2.185), and obesity (OR = 1.604) were independent risk factors for post-chemotherapy adverse events in HM patients (all P < 0.05). Based on the verified risk factors, a predictive model was proposed. This model had good discrimination and calibration. When 0.648 was selected as the cutoff point, the sensitivity and specificity of this predictive model in validation sets was 72.7 and 87.4 %, respectively. Furthermore, this proposed model's positive predictive value, negative predictive value, and consistency rate were 87.3, 73.0 and 80.0 %, respectively.

Our study indicated that this six risk factor-based mathematical model is accurate and sufficient enough to predict definite post-chemotherapy adverse events in a HM patient and it may aid clinicians to optimize treatment for a HM patient.