Nomogram for predicting neutropenia in patients with esophageal, gastric, or colorectal cancer treated by chemotherapy in the first cycle

Objectives Development and validation of a predictive model including serum vitamin concentration to estimate the risk of chemotherapy-induced grade 3/4 neutropenia in esophageal cancer, gastric cancer, or colorectal cancer patients who receive the first cycle of chemotherapy. Methods Data from 535 patients treated at the Affiliated Fuyang People's Hospital of Anhui Medical University from January 1, 2020, to March 2, 2022, were used to derive the predictive model. Least absolute shrinkage and selection operator regression analysis was performed to screen potential risk characteristics, and multivariate logistic regression was utilized to investigate efficient factors associated with chemotherapy-induced neutropenia. A nomogram was constructed using this logistic model. This nomogram was then tested on a temporal validation cohort containing 212 consecutive patients. Results In the cohort of all 747 eligible patients, grade 3/4 neutropenia incidence was 45.2%. Age, Eastern Cooperative Oncology Group-performance status, neutrophil count, serum albumin, and hemoglobin data were entered into the final model. The performance of the final predictive nomogram was assessed by the area under the receiver operating characteristic curve in both the development and validation datasets. The calibration curves indicated that the estimated risks were accurate. Decision curve analysis for the predictive model exhibited improved clinical practicality. Conclusion In the present study, we established an accessible risk predictive model and identified valuable serum vitamin concentration parameters associated with chemotherapy-induced neutropenia. The predictive model may improve the grade 3/4 neutropenia risk prediction in patients with gastrointestinal malignancies who receive oxaliplatin- and fluoropyrimidine-based chemotherapy and help physicians make appropriate decisions for disease management.