Metabolomics-based biomarker discovery for bee health monitoring: A proof of concept study concerning nutritional stress in Bombus terrestris

Background:Cross-sectoral coordination of treatment plans and efficient management of patients with chronic diseases and co-morbidities are of great importance. In rheumatoid arthritis (RA) it is essential to orchestrate information available for a patient at various locations, to allow (cost) efficient data use, to optimize management processes and to avoid redundant diagnostics. The information and communication platform developed in the Horizon2020 funded PICASO project (www.picaso-project.eu) supports the management of patients and their data along the continuum of care, consisting of hospitals, outpatient departments, practices, other health service providers via remote health monitoring. The platform might empower patients to improve their self-management of their illnesses.Objectives:What technological expertise and resources do RA patients and physicians have, who are willing to participate in a proof-of-concept study using a modern ICT platform? What is the user satisfaction? What are platform`s clinical implications?Methods:PICASO pursued a user-centered design approach. Platform`s user requirements were determined through workshops and interviews with physicians from various disciplines, patients and other stakeholders in the health care system (e.g. data protection officers). The development was accompanied by so-called “expert walkthroughs” to ensure a user-friendly design. An evaluation concept assessing the usability of the applications, user satisfaction and clinical relevance of the platform was part of the 6-month proof-of-concept study with RA patients and their physicians (rheumatologists and family doctors). A positive ethics vote was obtained.Results:111 user requirements were identified and used to develop the platform. Conformity with the GDPR as well as national regulations were precisely adhered to. All developments are based on the new ‘Fast Healthcare Interoperability Resources’ standard enabling data exchange with other software systems in the healthcare sector. This offers many advantages, e.g. a semantic model for describing the smallest units in the health care system (e.g. medication intake times, diagnostic procedures). Thus information can be linked and made available across sectors. Data can remain with the data owner and role-specific data access is ensured.30 RA patients (80% female) participated, mean age was 58.6±10.8 years, disease duration 12.6±8.5 years, DAS28 2.6±0.9, average number of comorbidities 3.0±1.6. Patients’ IT-experience was heterogeneous. After 6 months evaluations showed a good platform acceptance with an overall rating of 2.3±1.1 (n=27, Likert scale (LS) 1-6) and evaluation of ‘ease of use’ at 2.3±1.2 (n=27, LS 1-6). Usability tests showed that for patients the presentation of (1) tasks to be performed for the management of their disease, (2) results from their remote health monitoring, and (3) patient-reported outcome instruments in a dashboard was clear and easy to understand. Time required for documentation and daily tasks was rated as appropriate by 75.9% of the patients. No major technical problems or impairments due to RA where experienced when using the dashboard. 8 physicians (37.5 % female) participated in the evaluation; overall the platform was rated at 2.2±0.5 (LS 1-6).Conclusion:The platform offers cross-sectoral orchestration of patient data and thus innovative capabilities for modern management processes (e.g. treat-to-target, tele-monitoring). The PICASO platform is available for RA patients as well as for other chronic diseases.Acknowledgments:This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 689209.Disclosure of Interests:None declared

Download Full-text

Fitness-Tracker Assisted Frailty-Assessment Before Transcatheter Aortic Valve Implantation: Proof-of-Concept Study

JMIR mhealth and uhealth ◽

10.2196/19227 ◽

2020 ◽

Vol 8 (10) ◽

pp. e19227

Author(s):

Markus Mach ◽

Victoria Watzal ◽

Waseem Hasan ◽

Martin Andreas ◽

Bernhard Winkler ◽

...

Keyword(s):

Heart Rate ◽

Aortic Valve ◽

Hospital Mortality ◽

Health Monitoring ◽

Monitoring Device ◽

Proof Of Concept ◽

Concept Study ◽

Transcatheter Aortic Valve ◽

Frailty Assessment ◽

Fitness Tracker

Background While transcatheter aortic valve replacement (TAVR) has revolutionized the treatment of aortic valve stenosis, wearable health-monitoring devices are gradually transforming digital patient care. Objective The aim of this study was to develop a simple, efficient, and economical method for preprocedural frailty assessment based on parameters measured by a wearable health-monitoring device. Methods In this prospective study, we analyzed data of 50 consecutive patients with mean (SD) age of 77.5 (5.1) years and a median (IQR) European system for cardiac operative risk evaluation (EuroSCORE) II of 3.3 (4.1) undergoing either transfemoral or transapical TAVR between 2017 and 2018. Every patient was fitted with a wrist-worn health-monitoring device (Garmin Vivosmart 3) for 1 week prior to the procedure. Twenty different parameters were measured, and threshold levels for the 3 most predictive categories (ie, step count, heart rate, and preprocedural stress) were calculated. Patients were assigned 1 point per category for exceeding the cut-off value and were then classified into 4 stages (no, borderline, moderate, and severe frailty). Furthermore, the FItness-tracker assisted Frailty-Assessment Score (FIFA score) was compared with the scores of the preprocedural gait speed category derived from the 6-minute walk test (GSC-6MWT) and the Edmonton Frail Scale classification (EFS-C). The primary study endpoint was hospital mortality. Results The overall preprocedural stress level (P=.02), minutes of high stress per day (P=.02), minutes of rest per day (P=.045), and daily heart rate maximum (P=.048) as single parameters were the strongest predictors of hospital mortality. When comparing the different frailty scores, the FIFA score demonstrated the greatest predictive power for hospital mortality (FIFA area under the curve [AUC] 0.844, CI 0.656-1.000; P=.048; GSC-6MWT AUC 0.671, CI 0.487-0.855; P=.42; EFS-C AUC 0.636, CI 0.254-1.000; P=.44). Conclusions This proof-of-concept study demonstrates the strong predictive performance of the FIFA score compared to that of the conventional frailty assessments.

Download Full-text

Fitness-Tracker Assisted Frailty-Assessment Before Transcatheter Aortic Valve Implantation: Proof-of-Concept Study (Preprint)

10.2196/preprints.19227 ◽

2020 ◽

Author(s):

Markus Mach ◽

Victoria Watzal ◽

Waseem Hasan ◽

Martin Andreas ◽

Bernhard Winkler ◽

...

Keyword(s):

Heart Rate ◽

Aortic Valve ◽

Hospital Mortality ◽

Health Monitoring ◽

Monitoring Device ◽

Proof Of Concept ◽

Concept Study ◽

Transcatheter Aortic Valve ◽

Frailty Assessment ◽

Fitness Tracker

BACKGROUND While transcatheter aortic valve replacement (TAVR) has revolutionized the treatment of aortic valve stenosis, wearable health-monitoring devices are gradually transforming digital patient care. OBJECTIVE The aim of this study was to develop a simple, efficient, and economical method for preprocedural frailty assessment based on parameters measured by a wearable health-monitoring device. METHODS In this prospective study, we analyzed data of 50 consecutive patients with mean (SD) age of 77.5 (5.1) years and a median (IQR) European system for cardiac operative risk evaluation (EuroSCORE) II of 3.3 (4.1) undergoing either transfemoral or transapical TAVR between 2017 and 2018. Every patient was fitted with a wrist-worn health-monitoring device (Garmin Vivosmart 3) for 1 week prior to the procedure. Twenty different parameters were measured, and threshold levels for the 3 most predictive categories (ie, step count, heart rate, and preprocedural stress) were calculated. Patients were assigned 1 point per category for exceeding the cut-off value and were then classified into 4 stages (no, borderline, moderate, and severe frailty). Furthermore, the FItness-tracker assisted Frailty-Assessment Score (FIFA score) was compared with the scores of the preprocedural gait speed category derived from the 6-minute walk test (GSC-6MWT) and the Edmonton Frail Scale classification (EFS-C). The primary study endpoint was hospital mortality. RESULTS The overall preprocedural stress level (P=.02), minutes of high stress per day (P=.02), minutes of rest per day (P=.045), and daily heart rate maximum (P=.048) as single parameters were the strongest predictors of hospital mortality. When comparing the different frailty scores, the FIFA score demonstrated the greatest predictive power for hospital mortality (FIFA area under the curve [AUC] 0.844, CI 0.656-1.000; P=.048; GSC-6MWT AUC 0.671, CI 0.487-0.855; P=.42; EFS-C AUC 0.636, CI 0.254-1.000; P=.44). CONCLUSIONS This proof-of-concept study demonstrates the strong predictive performance of the FIFA score compared to that of the conventional frailty assessments.

Download Full-text

The Impact of Nutritional Supplementation on Sweat Metabolomic Content: A Proof-of-Concept Study

Frontiers in Chemistry ◽

10.3389/fchem.2021.659583 ◽

2021 ◽

Vol 9 ◽

Author(s):

Sean W. Harshman ◽

Andrew B. Browder ◽

Christina N. Davidson ◽

Rhonda L. Pitsch ◽

Kraig E. Strayer ◽

...

Keyword(s):

Performance Monitoring ◽

Human Performance ◽

Biomarker Discovery ◽

Exercise Program ◽

Area Under The Curve ◽

Nutritional Supplementation ◽

Abdominal Circumference ◽

Proof Of Concept ◽

Concept Study ◽

The Impact

Sweat is emerging as a prominent biosource for real-time human performance monitoring applications. Although promising, sources of variability must be identified to truly utilize sweat for biomarker applications. In this proof-of-concept study, a targeted metabolomics method was applied to sweat collected from the forearms of participants in a 12-week exercise program who ingested either low or high nutritional supplementation twice daily. The data establish the use of dried powder mass as a method for metabolomic data normalization from sweat samples. Additionally, the results support the hypothesis that ingestion of regular nutritional supplementation semi-quantitatively impact the sweat metabolome. For example, a receiver operating characteristic (ROC) curve of relative normalized metabolite quantities show an area under the curve of 0.82 suggesting the sweat metabolome can moderately predict if an individual is taking nutritional supplementation. Finally, a significant correlation between physical performance and the sweat metabolome are established. For instance, the data illustrate that by utilizing multiple linear regression modeling approaches, sweat metabolite quantities can predict VO2 max (p = 0.0346), peak lower body Windage (p = 0.0112), and abdominal circumference (p = 0.0425). The results illustrate the need to account for dietary nutrition in biomarker discovery applications involving sweat as a biosource.

Download Full-text