Cardiac-specific mortality prediction in adolescents and young adults with classical hodgkin lymphoma: a nomogram development and validation

Figures

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  Fig. 1. Cumulative incidence estimates of all-cause mortality among AYA patients with cHL (N = 10,680). Estimated using the Fine-Gray model.

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  Fig. 2. Cumulative incidence estimates of CSM among AYA patients with cHL, stratified by age, race, sex, marital status, year of diagnosis, Ann Arbor stage, tumor site, histologic type, radiotherapy and Chemotherapy (N = 10,680). Gray’s test was used for subgroup comparisons. CSM, cardiac-specific mortality.

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  Fig. 3. Forest plot of risk factors associated with CSM in multivariate competing risk analysis (N = 7,475). HRs and 95% CIs from the Fine-Gray model.

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  Fig. 4. Competing risk nomogram for predicting the 15- and 25- year cumulative incidence of CSM in cHL (N = 7,475). Constructed based on the Fine-Gray model.

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  Fig. 5. ROC curves for predicting the 15-, and 25-year probabilities of CSM in the training cohort (A, N = 7,475) and validation cohort (B, N = 3,205). Time-dependent ROC curves using the IPCW method. ROC, receiver operating characteristic; AUC, area under the curve.

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  Fig. 6. Time-dependent AUC curves for the nomogram predicting CSM in the training cohort (A, N = 7,475) and the validation cohort (B, N = 3,205). The x-axis “Time” indicates months after diagnosis. Time-dependent AUC values calculated with the IPCW method.

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  Fig. 7. Calibration curve for predicting the 15-, and 25-year probabilities of CSM in the training (A, B, N = 7,475) and validation cohort (C, D, N = 3,205), respectively. Calibration assessed by 10-fold cross-validation.

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  Fig. 8. Decision curves for predicting the 15-, and 25-year probabilities of CSM in the training (A, B, N = 7,475) and the validation cohort (C, D, N = 3,205), respectively. The x-axis represents the threshold probability, and the y-axis represents the net benefit. Decision curve analysis was performed to evaluate the clinical net benefit of the nomogram.