# Revisiting the OGIPRO Trial: Dynamic Electronic Patient-Reported Outcomes Compared with EQ-5D-5L in HER2-Positive Breast Cancer

## TL;DR

This study shows that digital patient-reported outcomes via smartphones closely match standard questionnaires for well-being in HER2-positive breast cancer patients, but symptoms need more detailed analysis.

## Contribution

The study demonstrates the reliability of dynamic ePROs for well-being and highlights the need for granular symptom analysis in digital patient monitoring.

## Key findings

- Dynamic ePRO well-being ratings strongly agree with EQ-5D-5L VAS scores.
- Aggregated ePRO symptom grades show weaker agreement with EQ-5D-5L domain sums.
- Digital PROs offer high-resolution data for personalized cancer care and AI applications.

## Abstract

Cancer treatments can affect patients’ daily lives in ways that are not always fully captured during clinic visits. To better understand how patients feel during therapy, doctors often use questionnaires, but these are usually completed only at fixed time points. New digital tools now allow patients to report their well-being and symptoms continuously using their smartphones, creating detailed, real-time pictures of how each patient is doing. In this study of people with a specific type of breast cancer, we compared these continuous digital patient reports with a widely used standard quality-of-life questionnaire. We found that patients’ self-reported overall well-being collected digitally closely matched the standard questionnaire results, showing that smartphone-based reporting can reliably reflect how patients feel. However, when many different symptoms were combined into a single summary score, the agreement was weaker, suggesting that symptoms need to be analyzed in a more detailed way. These findings show that digital patient reporting can provide reliable, high-resolution information about patients’ health. This type of data can improve how patients are monitored in everyday cancer care and support the future use of data-driven and AI-based tools to deliver more personalized, patient-centered treatment.

Introduction: Patient-reported outcomes (PROs) are increasingly valued in oncology for capturing treatment tolerability and quality of life, and they are emerging as important data sources for precision-medicine and AI-driven clinical workflows. While the EQ-5D-5L questionnaire remains a widely used standardized instrument, dynamic electronic PROs (ePROs) collected via mobile applications generate richer, higher-frequency longitudinal data. Their alignment with established PRO measures, however, is not well-understood, limiting their integration into routine care and downstream analytic applications. In the prospective OGIPRO trial (KEK-ZH 2021-D0051), patients with HER2-positive breast cancer reported well-being and symptoms via the Medidux ePRO platform alongside weekly EQ-5D-5L assessments. In this retrospective analysis, we used linear mixed-effects modeling to examine associations between: (i) dynamic ePRO well-being and the EQ-5D-5L visual analogue scale (VAS); (ii) dynamic ePRO symptom grades and EQ-5D-5L domain sums; (iii) ePRO symptom grades and EQ-5D-5L disutility using the EQ-5D-5L value set for Germany. Materials and Methods: The analytic dataset comprised 13,699 dynamic ePRO data points (3376 well-being ratings and 10,323 symptom grades across 91 symptom types) from 53 patients, forming high-frequency longitudinal patient trajectories. Of these, 252 and 226 time-aligned observations, respectively, were used for direct comparison with EQ-5D-5L VAS and domain scores. Results: Dynamic ePRO well-being showed strong agreement with EQ-5D-5L VAS (β = 1.061, 95% CI: 1.015–1.107), with low between-patient variability. In contrast, the agreement between aggregated ePRO symptom grades and EQ-5D-5L domain sums was weaker (β = 0.404, 95% CI: 0.307–0.501) and more heterogeneous across patients. The same applied to the agreement between ePRO symptom grades and EQ-5D-5L disutility (β = 0.213; 95% CI: 0.151–0.275). Discussion: Dynamic ePRO well-being aligns closely with EQ-5D-5L VAS scores, supporting its use as a pragmatic substitute in clinical and research settings. Aggregated symptom grades, however, showed limited concordance with EQ-5D-5L domains, indicating the need for more granular analyses on larger datasets. Conclusions: Overall, dynamic ePRO systems provide validated, high-resolution longitudinal patient data and represent a scalable foundation for patient monitoring and data-driven decision support in oncology, including future AI-based precision-medicine applications.