A novel model to predict the risk of hematological toxicity in lung adenocarcinoma patients with pemetrexed plus platinum chemotherapy based on real-world data.

BACKGROUND: Pemetrexed plus platinum chemotherapy is the first-line treatment option for lung adenocarcinoma. However, hematological toxicity is major dose-limiting and even life-threatening. The ability to anticipate hematological toxicity is of great value for identifying potential chemotherapy beneficiaries with minimal toxicity and optimizing treatment. The study aimed to develop and validate a prediction model for hematologic toxicity based on real-world data.

METHODS: Data from 1754 lung adenocarcinoma patients with pemetrexed plus platinum chemotherapy regimen as first-line therapy were used to establish and calibrate a risk model for hematological toxicity using multivariate and stepwise logistic regression analysis based on real-world data. The predictive performance of the model was tested in a validation cohort of 753 patients. An area under the curve (AUC) of the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis were used to assess the prediction model.

RESULTS: 5 independent factors (platinum, pre-use vitamin B12, cycle of chemotherapy before hematological toxicity, Hb before first chemotherapy, and PLT before first chemotherapy) identified from multivariate and stepwise logistic regression analysis were included in the prediction model. The hematological toxicity prediction model achieved a sensitivity of 0.840 and a specificity of 0.822. The model showed good discrimination in both cohorts (an AUC of 0.904 and 0.902 for the derivation and validation cohort ROC) at the cut-off value of 0.591. The calibration curve showed good agreement between the actual observations and the predicted results.

CONCLUSION: We developed a prediction model for hematologic toxicity with good discrimination and calibration capability in lung adenocarcinoma patients receiving a pemetrexed plus platinum chemotherapy regimen based on real-world data.