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Construction and validation of a nutritional status (CONUT)-based nomogram for predicting prolonged hematological
Peng Xu1,2,3, Xin-Ying Duan1,2,3, Qi-Wen Feng1,2,3
1Blood Disease Institute, Xuzhou Medical University, Xuzhou, Jiangsu, China.
Background Aim:
Chimeric antigen receptor (CAR)-T cell therapy is highly effective for relapsed/refractory multiple myeloma (R/R MM). Prolonged hematological toxicity (PHT) is a significant adverse event that adversely affects patient outcomes; however, specific predictive tools are lacking. Our prior study demonstrated that baseline Controlling Nutritional Status (CONUT) affects the prognosis of R/R MM patients receiving CAR-T cell therapy. We aimed to develop and validate a nomogram based on CONUT score for the early prediction of PHT after CAR-T cell therapy.
Methods:
This retrospective study included 302 consecutive patients with R/R MM who received CAR-T cell therapy. Patients were randomly allocated to training and validation cohorts (7:3 ratio). The primary endpoint was prolonged grade 3/4 neutropenia >28 days; predictors were identified using logistic regression. The model's performance was assessed by the area under the curve (AUC), calibration curves, and decision curve analysis (DCA).
Results:
Multivariable analysis confirmed four independent predictors for the primary endpoint (prolonged grade 3/4 neutropenia >28 days): high tumor burden (p = 0.013), ferritin (p = 0.002), interferon-γ (IFN-γ, p = 0.018), and CONUT score (p = 0.011). The nomogram built on these factors demonstrated a bias-corrected AUC of 0.815 in the training cohort, which was superior to the CAR-HEMATOTOX model (AUC: 0.706, p < 0.001). The predictive performance remained robust in the internal validation cohort (AUC: 0.824). The calibration curves showed good agreement between prediction and observation, and DCA confirmed the clinical utility of the model. The nomogram also exhibited excellent discriminative ability for predicting a composite PHT endpoint (AUC: 0.821, p = 0.417).
Conclusion:
We developed a validated nomogram that incorporates the baseline CONUT score and key clinical variables (e.g., tumor burden, ferritin, IFN-γ) to effectively predict PHT risk in R/R MM patients after CAR-T cell therapy, thereby facilitating early risk stratification and guiding personalized management.
