Predicting Absolute Risk of First Relapse in Classical Hodgkin Lymphoma by Incorporating Contemporary Treatment...

Area of Science:

• Hematology
• Oncology
• Biostatistics

Background:

• Individualized risk prediction is crucial for optimizing Hodgkin lymphoma (HL) treatment strategies.
• Current models often lack the integration of treatment effects, limiting their precision.

Purpose of the Study:

• To develop and validate prediction models for the absolute risk of progression, relapse, or death (PRD) in classical HL patients.
• To assess the added value of incorporating treatment information into PRD risk prediction models.

Main Methods:

• Utilized data from 2343 Dutch classical HL patients (2008-2018) and validated on 1675 Danish patients (2000-2018).
• Employed Cox proportional hazard models with time-varying coefficients to predict 5-year absolute PRD risk.
• Incorporated prognostic factors and treatment details (e.g., ABVD, BEACOPP, radiotherapy).

Main Results:

• In early-stage HL, gender, leukocyte, and lymphocyte counts were significant predictors. >4 cycles of ABVD or ABVD plus radiotherapy reduced relapse risk.
• In advanced-stage HL, age, albumin, and leukocyte counts predicted PRD risk. BEACOPP predicted lower PRD risk than ABVD.
• In external validation, incorporating treatment information improved the 5-year IPCW AUC for early stages (0.63 to 0.71, p=0.04) and advanced stages (0.59 to 0.62, p=0.33).

Conclusions:

• Developed well-calibrated models with reasonable discrimination for predicting absolute PRD risk in classical HL.
• Models effectively integrate pre-treatment prognostic factors and treatment effects for improved risk prediction.
• These models can aid in weighing treatment benefits against risks for individual HL patients.