Using Patient-Reported Outcome Measures to Predict Cancer Outcomes
Research type
Research Study
Full title
Using Patient-Reported Outcome Measures to Predict Long-Term Cancer Patient Outcomes
IRAS ID
325375
Contact name
Shazeea Masud
Contact email
Sponsor organisation
University of Leeds
Clinicaltrials.gov Identifier
n/a, n/a
Duration of Study in the UK
0 years, 9 months, 28 days
Research summary
Purpose: The purpose of this PhD is to determine whether patient questionnaire data can be integrated with routine healthcare data and, when combined, can be used to predict cancer patient outcomes, such as survival and hospital utilisation.
Application: This application is for the usage of the relevant data in the Hospital Episode Statistics (HES) dataset. This dataset contains routinely collected clinical data which will be used to predict the cancer patient outcomes, survival and hospital utilisation. This application is also for the organising and carrying out a series of focus groups with patients and clinicians separately.
Aim: This research aims to meet the needs of cancer survivors, by developing methods to improve the cancer outcomes that are central to health and social care.
Research Question: This project will investigate, “Does the inclusion of Patient-Reported Outcome Measures (PROMs) Add Predictive Value to Routine Healthcare Data when Predicting Long-Term Cancer Patient Outcomes?”.
Method: This study aims to develop and validate machine learning based artificial intelligence algorithms to enable accurate prediction of colorectal and prostate cancer patient survival, and later, healthcare utilisation. Our study design involves a human-in-the-loop methodology, which means we would like to create a dialogue in the form of focus groups, with patients and clinicians.
Data: The PROMs data that will be used has been collected by large scale studies on quality of life information from cancer survivors, including the "Living With and Beyond Bowel Cancer" survey (Downing et al., 2015) and the "Life After Prostate Cancer Diagnosis" study (LAPCD) (Downing et al., 2016). In this application, we are requesting ethical approval to use relevant clinical variables from the HES dataset in order to predict the patient cancer outcomes, survival and hospital utilisation.
Funding: The research is sponsored by the University of Leeds and is part of a PhD funded by the UKRI.
REC name
East Midlands - Nottingham 2 Research Ethics Committee
REC reference
24/EM/0066
Date of REC Opinion
15 Apr 2024
REC opinion
Further Information Favourable Opinion