Evaluation of the patient experience of symptomatic adverse events on Phase I clinical trials using PRO-CTCAE

Evaluating Immune-Related Adverse Events Using PRO-CTCAE in a Phase II Study of Ipilimumab for Hormone-Sensitive Prostate Cancer

Introduction

 Use of the Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) during chemotherapy is associated with decreased hospitalization rates, improved quality of life, and longer survival. Limited data exist on the benefit of this symptom assessment tool for monitoring immune-related adverse events (irAEs).

Methods

 We incorporated irAE-related items from the National Cancer Institute's (NCI) PRO-CTCAE in a trial evaluating ipilimumab in combination with androgen deprivation therapy in 16 patients with hormone-sensitive prostate cancer. For comparison, NCI's CTCAE version 4.0 was used by clinicians.

Results

 IrAE-related PRO-CTCAE surveys and matched CTCAEs (184 pairs) reporting abdominal pain, diarrhea, fatigue, anorexia, nausea, vomiting, rash, and pruritus were collected at each treatment administration and during follow-up. Fatigue, diarrhea, rash, and pruritus were the symptoms most frequently reported by both patients and clinicians. Agreement was lowest for pruritus (κ = 0.10) and highest for rash (κ = 0.64). IrAEs were more commonly reported and of higher grade with PRO-CTCAE scores compared with CTCAE grades.

Conclusion

 PRO-CTCAEs focused on irAEs capture the patient's immunotherapy experience while complementing the clinician's toxicity assessment measures. Further study is needed to assess PRO-CTCAE's utility in identifying and managing irAEs.

Adverse event signal extraction from cancer patients' narratives focusing on impact on their daily-life activities

Adverse event (AE) management is important to improve anti-cancer treatment outcomes, but it is known that some AE signals can be missed during clinical visits. In particular, AEs that affect patients' activities of daily living (ADL) need careful monitoring as they may require immediate medical intervention. This study aimed to build deep-learning (DL) models for extracting signals of AEs limiting ADL from patients' narratives. The data source was blog posts written in Japanese by breast cancer patients. After pre-processing and annotation for AE signals, three DL models (BERT, ELECTRA, and T5) were trained and tested in three different approaches for AE signal identification. The performances of the trained models were evaluated in terms of precision, recall, and F1 scores. From 2,272 blog posts, 191 and 702 articles were identified as describing AEs limiting ADL or not limiting ADL, respectively. Among tested DL modes and approaches, T5 showed the best F1 scores to identify articles with AE limiting ADL or all AE: 0.557 and 0.811, respectively. The most frequent AE signals were "pain or numbness", "fatigue" and "nausea". Our results suggest that this AE monitoring scheme focusing on patients' ADL has potential to reinforce current AE management provided by medical staff.

A tailored phase I-specific patient-reported outcome (PRO) survey to capture the patient experience of symptomatic adverse events

BACKGROUND

Patient perspectives are fundamental to defining tolerability of investigational anti-neoplastic therapies in clinical trials. Phase I trials present a unique challenge in designing tools for efficiently collecting patient-reported outcomes (PROs) given the difficulty of anticipating adverse events of relevance. However, phase I trials also offer an opportunity for investigators to optimize drug dosing based on tolerability for future larger-scale trials and in eventual clinical practice. Existing tools for comprehensively capturing PROs are generally cumbersome and are not routinely used in phase I trials.

METHODS

Here, we describe the creation of a tailored survey based on the National Cancer Institute's PRO-CTCAE for collecting patients' perspectives on symptomatic adverse events in phase I trials in oncology.

RESULTS

We describe our stepwise approach to condensing the original 78-symptom library into a modified 30 term core list of symptoms which can be efficiently applied. We further show that our tailored survey aligns with phase I trialists' perspectives on symptoms of relevance.

CONCLUSIONS

This tailored survey represents the first PRO tool developed specifically for assessing tolerability in the phase I oncology population. We provide recommendations for future work aimed at integrating this survey into clinical practice.