Home And Locally Observed - Tracking (HALO-Trak)

Overview

The goal of this observational study is to collect biometric, HRQoL, immune response and genomic data continuously and intermittently during and after chemo or immunotherapy for the generation of a complex dataset using a platform which can aggregate different types of data collected over a time period and, to test the potential for analysis within and across data sets with linkage to clinical outcomes. The framework will have capabilities to integrate data from electronic medical records (EMRs) such as Epic, as well as digital streams including sensor, genomic, imaging and pathology. Such a platform can realise the potential for machine learning (ML) methodologies to address important cancer outcomes.

The Investigators overarching aim is to determine the relationship between measures of physical performance status including heart rate and steps, health related quality of life (HRQoL) and genomic and immunogenomic phenotype on cancer outcomes in patients receiving chemotherapy or immunotherapy for haematological or metastatic cancer including renal (papillary, and RCC), breast, prostate, and other solid tumours. The primary objective is to test the feasibility of integrating diverse data streams (genomic, HR QoL and biometric) on a novel platform, capable of integrating data streams thus generating complex datasets for analyses using machine learning methodologies. Secondary objectives will be to conduct exploratory analysis assessing the relationship between individual and combined data types and cancer outcomes. Analysis using existing and novel computational models will be applied to the data to events in the acute and chronic setting that are common in patients diagnosed with cancer undergoing systemic therapy such as chemotherapy and immunotherapy. The outputs from this study will help inform future studies and trials designed to inform patients about their health status during cancer therapy

Conditions