Development and validation of a nomogram for predicting cardiovascular mortality risk for diffuse large B-cell lymphoma in children, adolescents, and adults - Mu, Kai; Zhang, Jing; Gu, Yan; Huang, Guoying (Frontiers in Pediatrics, 2024)
Objective: This study aimed to construct and validate a nomogram for predicting cardiovascular mortality (CVM) for children, adolescent, and adult patients with diffuse large B-cell lymphoma (DLBCL).: Patients with only one primary tumor of DLBCL first diagnosed between 2000 and 2019 in SEER database were extracted. We used cumulative incidence function (CIF) to evaluate the cumulative rate of CVM. The outcome of interest was CVM, which was analyzed by competing risk model accounting for death due to other causes. The total database was randomly divided into a training cohort and an internal validation cohort at a ratio of 7:3. Adjustments were for demographics, tumor characteristics, treatment modalities. Nomograms were constructed according to these risk factors to predict CVM risk at 5-, 10-, and 15-year. Validation included receiver operating characteristic (ROC) curves, time-dependent ROC, C-index, calibration curves and decision curve analysis.Results: 104,606 patients with first diagnose DLBCL were included (58.3% male, median age 64-year, range 0-80, White 83.98%). Among them, 5.02% died of CVM with a median follow-up time of 61 (31-98) months. Nomograms based on the seven risk factors (age at diagnosis, gender, race, tumor grade, Ann Arbor stage, radiation, chemotherapy) with hazard ratios ranging from 0.19-1.17 showed excellent discrimination and calibration plots demonstrated satisfactory prediction. The 5-, 10-, and 15year AUC and C-index of CVM in the training set were 0.716 (0.714-0.718), 0.713 (0.711-0.715), 0.706 (0.704-0.708), 0.731, 0.727, 0.719; the corresponding figures for the validation set were 0.705 (0.688-0.722), 0.704 (0.689-0.718), 0.707 (0.693-0.722),0.698, 0.698, 0.699. Decision curve analysis revealed a clinically beneficial net benefit.We first built the nomogram model for DLBCL patients with satisfactory prediction and excellent discrimination, which might play an essential role in helping physicians enact better treatment strategies at the time of initial diagnosis.