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

TL;DR

This paper introduces an automated method for selecting a minimal, relevant set of PRO-CTCAE symptoms for oncology trials by leveraging historical adverse event data and semantic analysis to optimize patient safety monitoring.

Contribution

The study presents a novel automated approach that uses MedDRA semantics and spectral analysis to efficiently select PRO-CTCAE items, reducing patient burden while maintaining safety signal detection.

Findings

The method effectively balances relevance and diversity in symptom selection.

It streamlines PRO-CTCAE design using semantic and historical data.

Performance validated through simulations and case studies.

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.