# Construction and validation of a nutritional status (CONUT)-based nomogram for predicting prolonged hematological toxicity in relapsed/refractory multiple myeloma after CAR-T cell therapy

## TL;DR

Researchers created a tool to predict prolonged blood-related side effects in multiple myeloma patients after CAR-T therapy, using a nutritional score and other clinical factors.

## Contribution

A novel nomogram integrating CONUT score and clinical variables for predicting prolonged hematological toxicity after CAR-T therapy in multiple myeloma patients.

## Key findings

- The nomogram achieved an AUC of 0.815 in training and 0.824 in validation for predicting prolonged neutropenia.
- CONUT score, tumor burden, ferritin, and IFN-γ were identified as independent predictors of prolonged hematological toxicity.
- The model outperformed the existing CAR-HEMATOTOX model and showed strong clinical utility via decision curve analysis.

## Abstract

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.

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).

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).

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.