Identifying responders to elamipretide in Barth syndrome: Hierarchical clustering for time series data

by Selleckchem | Sep 23 2023

Background: Barth syndrome (BTHS) is a rare genetic disease that is characterized by cardiomyopathy, skeletal myopathy, neutropenia, and growth abnormalities and often leads to death in childhood. Recently, elamipretide has been tested as a potential first disease-modifying drug. This study aimed to identify patients with BTHS who may respond to elamipretide, based on continuous physiological measurements acquired through wearable devices.

Results: Data from a randomized, double-blind, placebo-controlled crossover trial of 12 patients with BTHS were used, including physiological time series data measured using a wearable device (heart rate, respiratory rate, activity, and posture) and functional scores. The latter included the 6-minute walk test (6MWT), Patient-Reported Outcomes Measurement Information System (PROMIS) fatigue score, SWAY Balance Mobile Application score (SWAY balance score), BTHS Symptom Assessment (BTHS-SA) Total Fatigue score, muscle strength by handheld dynamometry, 5 times sit-and-stand test (5XSST), and monolysocardiolipin to cardiolipin ratio (MLCL:CL). Groups were created through median split of the functional scores into "highest score" and "lowest score", and "best response to elamipretide" and "worst response to elamipretide". Agglomerative hierarchical clustering (AHC) models were implemented to assess whether physiological data could classify patients according to functional status and distinguish non-responders from responders to elamipretide. AHC models clustered patients according to their functional status with accuracies of 60-93%, with the greatest accuracies for 6MWT (93%), PROMIS (87%), and SWAY balance score (80%). Another set of AHC models clustered patients with respect to their response to treatment with elamipretide with perfect accuracy (all 100%).

Conclusions: In this proof-of-concept study, we demonstrated that continuously acquired physiological measurements from wearable devices can be used to predict functional status and response to treatment among patients with BTHS.

Comments:

The study you mentioned aimed to investigate the potential of using continuously acquired physiological measurements from wearable devices to predict functional status and response to treatment in patients with Barth syndrome (BTHS). BTHS is a rare genetic disease that affects various body systems and often leads to severe health complications.

The researchers conducted a randomized, double-blind, placebo-controlled crossover trial involving 12 patients with BTHS. The participants wore wearable devices that collected physiological time series data, including heart rate, respiratory rate, activity level, and posture. The study also assessed various functional scores, such as the 6-minute walk test (6MWT), Patient-Reported Outcomes Measurement Information System (PROMIS) fatigue score, SWAY Balance Mobile Application score (SWAY balance score), BTHS Symptom Assessment (BTHS-SA) Total Fatigue score, muscle strength measured by handheld dynamometry, 5 times sit-and-stand test (5XSST), and the monolysocardiolipin to cardiolipin ratio (MLCL:CL).

To analyze the data, the researchers employed agglomerative hierarchical clustering (AHC) models. These models aimed to determine whether the physiological data could classify patients based on their functional status and distinguish between non-responders and responders to treatment with elamipretide, a potential disease-modifying drug for BTHS.

The AHC models successfully clustered patients according to their functional status with accuracies ranging from 60% to 93%. The highest accuracies were achieved for the 6MWT (93%), PROMIS fatigue score (87%), and SWAY balance score (80%). In addition, the models accurately clustered patients based on their response to elamipretide treatment, achieving a perfect accuracy rate of 100%.

The study's findings suggest that continuously acquired physiological measurements from wearable devices can be used to predict functional status and response to treatment in patients with BTHS. These results represent a proof-of-concept, highlighting the potential of using wearable technology and data analysis techniques to personalize treatment approaches for rare genetic diseases like BTHS.