Digital Biomarkers, a (too) brief introduction.pdf
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Presentation at the Artid workshop, U. Bristol, March 2024, on digital biomarkers for improved clinical trials and monitoring of complex diseases, including neurological & movement disorders.
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Digital Biomarkers, a (too) brief introduction.pdf
- 1. Digital Biomarkers A (too?) brief introduction Paul Agapow Statistics & Data Science Innovation Hub, GSK
- 2. The obligatory background & disclaimer Who am I? Once an immunologist Then a molecular evolutionist, modeller, bioinformatician, epi-informatician, analyst, data scientist, computer guy ... Now solve clinical & trial problems for big pharma using data, stats & AIML Nothing in this presentation represents projects or policy at GSK There are no con鍖icts of interest
- 3. What is a digital biomarker? The broad (and oft ignored) de鍖nition Anything measured and recorded digitally, even: Blood pressure Height Heart rate Online survey Phone apps ... The narrow (and stereotypical) de鍖nition Digital recording of complex patient behaviour and physiology, followed by processing to produce quanti鍖able metrics or endpoints, perhaps via a device or sensor carried by the patient: Using a wearable to track a patients heart rate over time Videoing a patient walking and modelling it to produce a quality of locomotion Sound analysis of voice or a cough, to detect obstruction
- 4. Example: Parkinson's Disease 1 Deng, K., Li, Y., Zhang, H. et al. Heterogeneous digital biomarker integration out-performs patient self-reports in predicting Parkinsons disease. Commun Biol 5, 58 (2022). https://doi.org/10.1038/s42003-022-03 002-x
- 5. Example: Parkinson's Disease 2 Interpretable Video-Based Tracking and Quantification of Parkinsonism Clinical Motor States Daniel Deng, Jill L. Ostrem, Vy Nguyen, Daniel D. Cummins, Julia Sun, Anupam Pathak, Simon Little, Reza Abbasi-Asl medRxiv 2023.11.04.23298083; doi: https://doi.org/10.1101/2023.11.04.23298083
- 6. Why use digital biomarkers? Fidelity: avoids transcription errors Memory: not reliant upon patient recall Consistency: across sites, investigators, geographies Authenticity: measure in a real-world context, away from clinical visits Patient-centricity Temporality: measures across time, perhaps to assess average behaviour or look for rare events Safety: monitoring patients for AEs Simplicity: distil complex behaviour and physiology to something simpler and measurable
- 7. Why not use digital biomarkers Does it actually work for the patients Some patients like going to the clinic Also additional medical support Hardware & infrastructure & support Privacy Standardization Abstract, intangible measures Machine learning magic
- 8. Validity Analytical Does it take good measurements? Does it measure something robustly and reproducibly? Can it cope with patchy or imperfect data? Do small changes in signal or source only result in Clinical Does this measure mean something? Does a change in the digital measure relate to a change in disease state? A form of experimenters regress?
- 9. Summary Digital biomarkers are any persistent digital measurement of patients Such biomarkers can be used reduce complex behaviours (e.g. movement) to simpler and more quanti鍖able endpoints But to do this we need hardware solutions and good data analytics