Clinically actionable insights into endometriosis symptom trajectories using longitudinal self-reports, biological samples, and data from digital technologies

Background

Endometriosis is a chronic condition associated with debilitating pain, fatigue, and heterogeneous symptom manifestation. It affects ~10% women of reproductive age, may take ~8 years to diagnose, and symptom progression typically relies on sparse clinical assessments. There is an urgent call for action to capitalize on recent biological and technical developments to improve diagnosis and symptom monitoring [1]. We have recently proposed developing a pioneering framework to transform endometriosis assessment capitalizing on digital technologies [2]. Standardised patient reported outcome measures (PROMs) where people living with endometriosis regularly self-report on their symptoms are increasingly used to monitor symptom severity progression. Over time, these can provide useful insights into patients’ own self perception of pain and diverse symptoms. Similarly, regularly collected biological samples may Page 9 of 14 offer insights into symptom trajectories over time. The proliferation of new digital health technologies, including wearable sensors, has been gaining increasing momentum [2]. The use of digital health technologies can provide additional continuous and passively collected data, which can be mined to obtain new insights complementing clinical reports, lab tests, and PROMs. We recently reported on the largest study of-its-kind endometriosis study, demonstrating how self-reports and wearable sensors can provide longitudinal insights into symptom trajectories and objective surgical intervention assessments [3]. Specifically, we have developed new signal processing and statistical machine learning algorithms towards assessing physical activity, sleep, and diurnal rhythm variability to process actigraphy data, and demonstrated how the extracted characteristics could complement and inform clinical assessments. Additionally, we have been developing information fusion and deep learning algorithms to mine multimodal data in other research applications [4], which could be deployed in the endometriosis project too. Building on projects that the supervisory team is involved (e.g. the £4m ADVANTAGE project and the £6m EUmetriosis project) this is an exciting time as we have collected (with further data collection ongoing) the largest longitudinal multimodal datasets in endometriosis.