Automated PRO-CTCAE Symptom Selection based on Prior Adverse Event Profiles

TL;DR

This work tackles the PRO-CTCAE item selection problem in oncology trials by introducing an automated, semantics-driven pipeline that maps PRO terms to MedDRA, embeds them with Safeterm, and uses a relevance–diversity framework with a spectral L-Kernel to identify a minimal yet comprehensive symptom set. The method balances historical safety signal coverage with semantic diversity to reduce patient burden, and it is deterministic and faster than iterative sampling approaches like DPPs. Its performance is demonstrated through large-scale simulations and a retrospective multiple myeloma case study, showing robust recovery of relevant symptoms and AE coverage comparable to manual selections. The approach provides an objective, reproducible framework for PRO-CTCAE design that can streamline trial design and improve the quality of safety signal capture.

Abstract

The PRO-CTCAE is an NCI-developed patient-reported outcome system for capturing symptomatic adverse events in oncology trials. It comprises a large library drawn from the CTCAE vocabulary, and item selection for a given trial is typically guided by expected toxicity profiles from prior data. Selecting too many PRO-CTCAE items can burden patients and reduce compliance, while too few may miss important safety signals. We present an automated method to select a minimal yet comprehensive PRO-CTCAE subset based on historical safety data. Each candidate PRO-CTCAE symptom term is first mapped to its corresponding MedDRA Preferred Terms (PTs), which are then encoded into Safeterm, a high-dimensional semantic space capturing clinical and contextual diversity in MedDRA terminology. We score each candidate PRO item for relevance to the historical list of adverse event PTs and combine relevance and incidence into a utility function. Spectral analysis is then applied to the combined utility and diversity matrix to identify an orthogonal set of medical concepts that balances relevance and diversity. Symptoms are rank-ordered by importance, and a cut-off is suggested based on the explained information. The tool is implemented as part of the Safeterm trial-safety app. We evaluate its performance using simulations and oncology case studies in which PRO-CTCAE was employed. This automated approach can streamline PRO-CTCAE design by leveraging MedDRA semantics and historical data, providing an objective and reproducible method to balance signal coverage against patient burden.

Figures (2)

• Figure 1: Safeterm 2-D map of PRO-CTCAE symptoms classified by category.
• Figure 2: Maximum similarity among 80 PRO-CTCAE symptoms versus True Positive Incidence Rate (Recall from 1,000 simulations).