BrainWear – correlating wearable data with clinical outcomes in brain tumour patients
A New Year turns, and with it brings my very first blog post for the Computational Oncology Group, in poll position on Matt Williams highly anticipated ‘2021 blog rota’… I am the chosen one. I will use this opportunity to share with you some highlights and lessons from my PhD thus far, which I hope gives you an insight into my work within the lab and with our patients, for whom we strive to deliver.
BrainWear (ISRCTN34351424 https://doi.org/10.1186/ISRCTN34351424) is a clinical study collecting wearable data in the form of a wrist worn Axivity AX3 accelerometer from patients with primary and secondary brain tumours. It is the brainchild of one of our very own patients who wanted to share his wearable data with us whilst having treatment as an additional monitoring tool. One of the strengths in traversing both a computational and clinical PhD is learning that patients are an invaluable source of knowledge and have undoubtedly helped shape the research we do and the questions we ask from our data. Our first BrainWear patient was recruited in October 2018 and we have since recruited 60+ patients, with some providing > 1 year of accelerometer data and for the first time we will be able to objectively understand how daily activity changes with surgery, chemoradiotherapy and at the point of disease progression. In its essence, BrainWear is a feasibility study and asks the simple questions: is it possible for brain tumour patients to wear a wrist worn device whilst having treatment? do they find it acceptable? Is longitudinal data collection possible? But as we delve deeper into this first of its kind dataset, there are layers of information about patient physical activity levels, sleep pattern, gait (walking style) and quality of life in simple accelerometer data. The passive nature of the data (collected without interference from neither patient nor clinician) represents the patient objectively, and it is this aspect of the data and its correlation with existing clinical parameters which I have found so attractive. Our work is now to understand how these data change over time and with disease activity but more interestingly whether there are any early indicators to identify ill health i.e., disease progression or hospitalisation. Can this data be used to explain, influence and/or predict health-related outcomes and in turn be translated into a digital biomarker to guide clinical decision making?
Accelerometery data captured at a sampling frequency of 100Hz (100 readings per second) rapidly expands in size and requires powerful data analysis and manipulation tools, for which I have utilised Pandas built on top of Python 3, and the watchful eye of our lab data analyst. Whilst keen to explore how we can use machine learning (ML) methods as a predictive method of worsening disease particularly around gait and sleep in those patients with high grade gliomas, I am currently taking some time to understand the more traditional statistical methods of longitudinal data analysis utilising mixed effects models. A recent systematic review by Christodoulou et al showed no performance benefit of machine learning over logistic regression for clinical predictive models in low dimensional data. I believe one of the strengths in training clinicians to manipulate and analyse data computationally is our ability to understand the clinical impact of the question being asked from the data, and when relaying our findings to the clinical community, it is important that our methods are robust and represent the more traditional statistical approaches as well as novel ML methods. We have however taken the opportunity to capitalise on the ease of access to accelerometer data with our MSc students, and developed support vector machine classifiers for identifying pure walking bouts and extend on existing work done using neural networks to classify clean walking and gait characteristics. The UK Biobank and the work done by Aiden Doherty’s team in processing and publishing findings on just under 100,000 participants 7-day accelerometery readings have given us the opportunity to compare patients with high grade glioma to healthy UK biobank participants. This will allow us to understand in greater depth how patients with brain tumours are impacted by their disease.
Digital remote health monitoring is becoming more mainstream in clinical and trials settings, fuelled by rapid development in sensor technology and in trying to provide increasingly patient centred care, particularly in the era of COVID-19. Correctly utilising these devices and data has the potential to provide a method of monitoring the patient in motion rather than the episodic snapshot we currently see and in turn improve our clinical decision making and patient outcomes. In that spirit, in my next blog post I hope to update you on some preliminary analysis from our high grade glioma patient subset of BrainWear, and will discuss how I am going to tackle the non-trivial task of incorporating wearable data into clinical decision making models.
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