Discussion
AHC, fatigue, BTHS
RARE DISEASE
The present study showed that continuously acquired physiological measurements such as HR, RR, and activity-related metrics acquired using wearable devices can be used to predict (i) functional status and (ii) response to treatment with elamipretide in patients with BTHS. In the first part of the study, AHC models clustered patients according to their functional status with accuracies of 60–93% based on a mean of 218 PVs, with the greatest accuracies for 6MWT (93%), PROMIS fatigue score (87%), and SWAY balance score (80%). In the second part of the study, another set of AHC models clustered patients with respect to their response to treatment with elamipretide with perfect accuracy (all 100%) based on a mean of 125 PVs. Collectively, these findings suggest that the application of machine learning-based techniques to data from wearable devices may open new frontiers in patient phenotyping, monitoring and stratification.While randomized controlled trials can be used to establish efficacy and safety of a therapy at the population level, at most they can generate data about the “average treatment effects” that would apply for an “average patient in the trial population” [With the widespread availability of wearable devices, a large amount of physiological data has become immediately accessible [It is notable that certain PVs were more closely related to functional status and/or treatment response than others. Interestingly, for each of the AHC models, PVs based on max and mean HR values were more commonly included than those based on mean HR (Fig. The major strength of this study is its innovative application of unsupervised machine learning (AHC models) to cluster patients according to their functional status or treatment response. The use of unsupervised machine learning to create a prediction model in a very rare disease is a novel approach as this is a substantial analytic challenge. Traditional machine learning approaches (including deep learning) usually require large datasets for algorithm training which tend to preclude their use in rare disease. Unsupervised machine learning, on the contrary, uses feature similarity between patients to cluster patients and uncover underlying patterns based on those clusters. This allows for prediction of outcomes for new patients based on their feature similarity to existing examples.The models demonstrated excellent accuracy for a total of 7 different outcomes, covering a spectrum of important functional markers in patients with BTHS. Furthermore, an extensive set of features based was screened when constructing each of the AHC models, allowing for the most predictive ones to be selected. More specifically, the use of higher order features (derived using the “tsfresh” package) from time series data lends itself well for classification or clustering purposes, allowing to pack existing data variability within only a handful of features specific to different segments of the population. In addition, all time series data were obtained using the AVIVO™ mobile patient management system (Medtronic Inc.), which required no efforts from the patients except for wearing it for 7 consecutive days.However, there are some limitations which need to be considered. First, because BTHS is an extremely rare disease, our study could only include 10 patients. Second, our study should be regarded as an exploratory proof-of-concept study. Since accuracy was calculated based on the same data that were used for training, our estimates might be optimistic, and external validation of our findings is warranted before clinical use. Third, while dichotomous endpoints based on median split were used (“highest value” versus “lowest value”), we did not test whether the exact values for functional status and treatment response could be predicted by the AHC models. Finally, we did not test whether data from the more commonly used smartwatches allowed for similarly accurate AHC models; this will require further investigation.