Parkinson's ex machina

Last week Anna talked about the need to ensure the 'human' is kept at the centre of drug design for Parkinson's treatments - "focussing on human cells, tissues and subjects". Today I also want to discuss the role of humans in Parkinson's and the role of machines.

Neurology (and neurologists) take pride from being a bastion of clinical medicine - the heart and soul of our practice is listening to our patient and observing their every movement. In some ways neurology has not moved on much (in spirit) from the historic view of 'physicians' portrayed so poignantly by Sir Luke Fields. Movement disorders, and the diagnosis and management of Parkinson's are perfect examples of this human approach, with no equipment other than a tendon hammer necessary to make the diagnosis.

taken from https://www.tate.org.uk/art/artworks/fildes-the-doctor-n01522 with thanks

However, today's world has moved on leaps and bounds, and medicine and neurology are desperately trying to keep pace. In the 130 years since this scene was painted, our world has changed almost unrecognisably (although a Google image search of 'doctor' would make anyone believe that it is still delivered primarily by middle aged white men - which thankfully is less true now than ever).

It is old hat to say that technology continues at a blistering pace. Miniaturisation  has made yesterday's science fiction into the everyday. 'The Fantastic Voyage' described a vessel that was shrunk and inserted into a person's body to save them - well thrombectomy for stroke, stunting for heart attacks, and a video camera so small you can swallow it to get diagnostic pictures throughout the gut are all in routine clinical practice in the NHS today.

The Fantastic Voyage (1966) - picture from https://www.britannica.com/topic/Fantastic-Voyage

High street technology has revolutionised our lives. There is more processing power in my 4 year old iPhone than Armstrong, Aldrin and Collins had on Apollo 11. There is now an enormous market in wearable technology that will give you a beat-by-beat analysis of your heart rate, count your steps in a day, your calorie output, measure your power in watts on a bike, feet climbed on the stairs and just about any other metric you can imagine.

Just a small number of the dizzying array of activity tracking watches available on the high street (picture from  Groupon.co.uk)

All this brings me onto a review I have read, "Optimizing clinical assessments in Parkinson's disease through the use of wearable sensors and data driven modelling"

The article offers a review on some of the hardware software and statistical approaches to understanding the data. At its heart is the understanding that a neurologist sees their patient for perhaps 3 or 4 15-20 appointments in a year. That clearly leaves 8,759 hours per year where the neurologist has no ability to observe their patient move. Wearable technology brings us a (relatively) non-intrusive window into the 'real lives' of our patients. With the vast amounts of data that this can bring, we need very sophisticated algorithms to understand it, and we need to understand the computer science and mathematical constructs that underlie those algorithms.

Going further than mere observation is prediction - the holy grail in healthcare, and our clearly stated aim in PREDICT-PD. This article concludes by cautioning us about the variation in quality and output of this technology, but offering us the hope that these devices and underlying data analysis will improve clinical decision making, provide more personalised care, and better understand and highlight the differences in the manifestations of what we currently call 'Parkinson's disease', leading to better trials, treatments and one day - cure.

RNR

Using Smartphones and Machine Learning to Quantify Parkinson Disease Severity: The Mobile Parkinson Disease Score

Smart phone technology and app-based approaches for measuring PD symptoms and signs have been around for some time and there are seemingly innumerable companies and groups that all suggest that their approach is the best...

The results of this recent study published in JAMA Neurology do however look really impressive. I have always felt that given the heterogeneity of PD (motor and non-motor), a good measuring device will take account of multiple domains. This app measures voice, gait, balance, reaction time and finger tapping (which you might argue are exclusively motor)... but the results suggest that it does measure these really well. 

The hardest thing is to develop a tool that captures fluctuation well and objectively. Some purpose-built devices do (the PKG for example), but it is impressive to see these kind of results through utilisation of the standard hardware that comes with a smart phone. Furthermore the objective response to dopaminergic therapy is substantial and apparently clinically meaningful. 

Will we see this app used in clinical trials in the coming years... I expect we might!

- Alastair Noyce

JAMA Neurol. 2018 Mar 26. doi: 10.1001/jamaneurol.2018.0809. [Epub ahead of print]
Zhan A, Mohan S, Tarolli C, Schneider RB, Adams JL, Sharma S, Elson MJ, Spear KL, Glidden AM, Little MA, Terzis A, Dorsey ER, Saria S.

https://jamanetwork.com/journals/jamaneurology/fullarticle/2676504

IMPORTANCE: Current Parkinson disease (PD) measures are subjective, rater-dependent, and assessed in clinic. Smartphones can measure PD features, yet no smartphone-derived rating score exists to assess motor symptom severity in real-world settings.

OBJECTIVES: To develop an objective measure of PD severity and test construct validity by evaluating the ability of the measure to capture intraday symptom fluctuations, correlate with current standard PD outcome measures, and respond to dopaminergic therapy.

DESIGN, SETTING, AND PARTICIPANTS: This observational study assessed individuals with PD who remotely completed 5 tasks (voice, finger tapping, gait, balance, and reaction time) on the smartphone application. We used a novel machine-learning-based approach to generate a mobile Parkinson disease score (mPDS) that objectively weighs features derived from each smartphone activity (eg, stride length from the gait activity) and is scaled from 0 to 100 (where higher scores indicate greater severity). Individuals with and without PD additionally completed standard in-person assessments of PD with smartphone assessments during a period of 6 months.

MAIN OUTCOMES AND MEASURES: Ability of the mPDS to detect intraday symptom fluctuations, the correlation between the mPDS and standard measures, and the ability of the mPDS to respond to dopaminergic medication.

RESULTS: The mPDS was derived from 6148 smartphone activity assessments from 129 individuals (mean [SD] age, 58.7 [8.6] years; 56 [43.4%] women). Gait features contributed most to the total mPDS (33.4%). In addition, 23 individuals with PD (mean [SD] age, 64.6 [11.5] years; 11 [48%] women) and 17 without PD (mean [SD] age 54.2 [16.5] years; 12 [71%] women) completed in-clinic assessments. The mPDS detected symptom fluctuations with a mean (SD) intraday change of 13.9 (10.3) points on a scale of 0 to 100. The measure correlated well with the Movement Disorder Society Unified Parkinson Disease's Rating Scale total (r = 0.81; P < .001) and part III only (r = 0.88; P < .001), the Timed Up and Go assessment (r = 0.72; P = .002), and the Hoehn and Yahr stage (r = 0.91; P < .001). The mPDS improved by a mean (SD) of 16.3 (5.6) points in response to dopaminergic therapy.

CONCLUSIONS AND RELEVANCE: Using a novel machine-learning approach, we created and demonstrated construct validity of an objective PD severity score derived from smartphone assessments. This score complements standard PD measures by providing frequent, objective, real-world assessments that could enhance clinical care and evaluation of novel therapeutics.