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

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.

Lung Ultrasound Findings in Healthy Children and in Those Who Had Recent, Not Severe COVID-19 Infection.

BACKGROUND: Lung ultrasound (LUS) is gaining consensus as a non-invasive diagnostic imaging method for the evaluation of pulmonary disease in children. AIM: To clarify what type of artifacts (e.g., B-lines, pleural irregularity) can be defined normal LUS findings in children and to evaluate the differences in children who did not experience COVID-19 and in those with recent, not severe, previous COVID-19. METHODS: LUS was performed according to standardized protocols. Different patterns of normality were defined: pattern 1: no plural irregularity and no B-lines; pattern 2: only mild basal posterior plural irregularity and no B-lines; pattern 3: mild posterior basal/para-spine/apical pleural irregularity and no B-lines; pattern 4: like pattern 3 plus rare B-lines; pattern 5: mild, diffuse short subpleural vertical artifacts and rare B-lines; pattern 6: mild, diffuse short subpleural vertical artifacts and limited B-lines; pattern 7: like pattern 6 plus minimal subpleural atelectasis. Coalescent B-lines, consolidations, or effusion were considered pathological. RESULTS: Overall, 459 healthy children were prospectively recruited (mean age 10.564 ± 3.839 years). Children were divided into two groups: group 1 (n = 336), those who had not had COVID-19 infection, and group 2 (n = 123), those who experienced COVID-19 infection. Children with previous COVID-19 had higher values of LUS score than those who had not (p = 0.0002). Children with asymptomatic COVID-19 had similar LUS score as those who did not have infections (p > 0.05), while those who had symptoms showed higher LUS score than those who had not shown symptoms (p = 0.0228). CONCLUSIONS: We report the pattern of normality for LUS examination in children. We also showed that otherwise healthy children who recovered from COVID-19 and even those who were mildly symptomatic had more "physiological" artifacts at LUS examinations.

Cardiothoracic robotic assisted surgery in times of COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic poses an immense threat to healthcare systems worldwide. At a time when elective surgeries are being suspended and questions are being raised about how the remaining procedures on COVID-19 positive patients can be performed safely, it is important to consider the potential role of robotic assisted surgery within the current pandemic. Recently, several robotic assisted surgery societies have issued their recommendations. To date, however, no specific recommendations are available for cardiothoracic robotic assisted surgery in COVID-19 positive patients. Here, we discuss the potential risks, benefits, and preventive measures that need to be taken into account when considering robotic assisted surgery for cardiothoracic indications in patients with confirmed COVID-19. It is suggested that robotic assisted surgery might have various advantages such as early recovery after surgery, shorter hospital stay, and reduced loss of blood and fluids as well as smaller incisions. However, electrosurgical and ultrasonic devices, as well as CO2 insufflation should be managed with caution to prevent the risk of aerosolization of viral particles.