Body composition analysis in patients with acute heart failure: the Scale Heart Failure trial.

AIMS: In this study, we aimed to investigate whether body composition analysis (BCA) derived from bioelectrical impedance vector analysis (BIVA) could be used to monitor the hydration status of patients with acute heart failure (AHF) during intensified diuretic therapy. METHODS AND RESULTS: This observational, single-centre study involved a novel, validated eight-electrode segmental body composition analyser to perform BCA derived from BIVA with an alternating current of 100 µA at frequencies of 5, 7.5, 50, and 75 kHz. The BCA-derived and BIVA-derived parameters were estimated and compared with daily body weight measurements in hospitalized patients with AHF. A total of 867 BCA and BIVA assessments were conducted in 142 patients (56.3% men; age 76.8 ± 10.7 years). Daily changes in total body water (TBW) and extracellular water (ECW) were significantly associated with changes in body weight in 62.2% and 89.1% of all measurements, respectively (range, ±1 kg). Repeated measures correlation coefficients between weight loss and TBW loss resulted with rho 0.43, P < 0.01, confidence interval (CI) [0.36, 0.50] and rho 0.71, P > 0.01, CI [0.67, 0.75] for ECW loss. Between the first and last assessments, the mean weight loss was -2.5 kg, compared with the -2.6 L mean TBW loss and -1.7 L mean ECW loss. BIVA revealed an increase in mean Resistance R and mean Reactance Xc across all frequencies, with the subsequent reduction in body fluid (including corresponding body weight) between the first and last assessments. CONCLUSIONS: Body composition analysis derived from BIVA with a focus on ECW is a promising approach to detect changes in hydration status in patients undergoing intensified diuretic therapy. Defining personalized BIVA reference values using bioelectrical impedance devices is a promising approach to monitor hydration status.

Next-Generation Digital Biomarkers for Tuberculosis and Antibiotic Stewardship: Perspective on Novel Molecular Digital Biomarkers in Sweat, Saliva, and Exhaled Breath.

The internet of health care things enables a remote connection between health care professionals and patients wearing smart biosensors. Wearable smart devices are potentially affordable, sensitive, specific, user-friendly, rapid, robust, lab-independent, and deliverable to the end user for point-of-care testing. The datasets derived from these devices are known as digital biomarkers. They represent a novel patient-centered approach to collecting longitudinal, context-derived health insights. Adding automated, analytical smartphone applications will enable their use in high-, middle-, and low-income countries. So far, digital biomarkers have been focused primarily on accelerometer data and heart rate due to well-established sensors originating from the consumer market. Novel emerging smart biosensors will detect biomarkers (or compounds) independent of a lab and noninvasively in sweat, saliva, and exhaled breath. These molecular digital biomarkers are a promising novel approach to reduce the burden from 2 major infectious diseases with urgent unmet needs: tuberculosis and infections with multidrug resistant pathogens. Active tuberculosis (aTbc) is one of the deadliest diseases from an infectious agent. However, a simple and reliable test for its detection is still missing. Furthermore, inappropriate antimicrobial use leads to the development of antimicrobial resistance, which is associated with high mortality and health care costs. From this perspective, we discuss the innovative approach of a noninvasive and lab-independent collection of novel biomarkers to detect aTbc, which at the same time may additionally serve as a scalable therapeutic drug monitoring approach for antibiotics. These molecular digital biomarkers are next-generation digital biomarkers and have the potential to shape the future of infectious diseases.

The Detection of Vancomycin in Sweat: A Next-Generation Digital Surrogate Marker for Antibiotic Tissue Penetration: A Pilot Study.

BACKGROUND: Assuring adequate antibiotic tissue concentrations at the point of infection, especially in skin and soft tissue infections, is pivotal for an effective treatment and cure. Despite the global issue, a reliable AB monitoring test is missing. Inadequate antibiotic treatment leads to the development of antimicrobial resistances and toxic side effects. ß-lactam antibiotics were already detected in sweat of patients treated with the respective antibiotics intravenously before. With the emergence of smartphone-based biosensors to analyse sweat on the spot of need, next-generation molecular digital biomarkers will be increasingly available for a non-invasive pharmacotherapy monitoring. OBJECTIVE: Here, we investigated if the glycopeptide antibiotic vancomycin is detectable in sweat samples of in-patients treated with intravenous vancomycin. METHODS: Eccrine sweat samples were collected using the Macroduct Sweat Collector®. Along every sweat sample, a blood sample was taken. Bio-fluid analysis was performed by Ultra-high Pressure Liquid Chromatograph-Tandem Quadrupole Mass Spectrometry coupled with tandem mass spectrometry. RESULTS: A total of 5 patients were included. Results demonstrate that vancomycin was detected in 5 out of 5 sweat samples. Specifically, vancomycin concentrations ranged from 0.011 to 0.118 mg/L in sweat and from 4.7 to 8.5 mg/L in blood. CONCLUSION: Our results serve as proof-of-concept that vancomycin is detectable in eccrine sweat and may serve as a surrogate marker for antibiotic tissue penetration. A targeted vancomycin treatment is crucial in patients with repetitive need for antibiotics and a variable antibiotic distribution such as in peripheral artery disease to optimize treatment effectiveness. If combined with on-skin smartphone-based biosensors and smartphone applications, the detection of antibiotic concentrations in sweat might enable a first digital, on-spot, lab-independent and non-invasive therapeutic drug monitoring in skin and soft tissue infections.

Device- and Analytics-Agnostic Infrastructure for Continuous Inpatient Monitoring: A Technical Note.

The internet of healthcare things aims at connecting biosensors, clinical information systems and electronic health dossiers. The resulting data expands traditionally available diagnostics with digital biomarkers. In this technical note, we report the implementation and pilot operation of a device- and analytics-agnostic automated monitoring platform for in-house patients at hospitals. Any available sensor, as well as any analytics tool can be integrated if the application programming interface is made available. The platform consists of a network of Bluetooth gateways communicating via the hospital's secure Wi-Fi network, a server application (Device Hub) and associated databases. Already existing access points or low-cost hardware can be used to run the gateway software. The platform can be extended to a remote patient monitoring solution to close the gap between in-house treatments and follow-up patient monitoring.

Non-invasive Drug Monitoring of ß-Lactam Antibiotics Using Sweat Analysis-A Pilot Study.

Background: Antimicrobial resistance is a major challenge in treating infectious diseases. Therapeutic drug monitoring (TDM) can optimize and personalize antibiotic treatment. Previously, antibiotic concentrations in tissues were extrapolated from skin blister studies, but sweat analyses for TDM have not been conducted. Objective: To investigate the potential of sweat analysis as a non-invasive, rapid, and potential bedside TDM method. Methods: We analyzed sweat and blood samples from 13 in-house patients treated with intravenous cefepime, imipenem, or flucloxacillin. For cefepime treatment, full pharmacokinetic sampling was performed (five subsequent sweat samples every 2 h) using ultra-high-performance liquid chromatography coupled with triple quadrupole mass spectrometry. The ClinicalTrials.gov registration number is NCT03678142. Results: In this study, we demonstrated for the first time that flucloxacillin, imipenem, and cefepime are detectable in sweat. Antibiotic concentration changes over time demonstrated comparable (age-adjusted) dynamics in the blood and sweat of patients treated with cefepime. Patients treated with standard flucloxacillin dosage showed the highest mean antibiotic concentration in sweat. Conclusions: Our results provide a proof-of-concept that sweat analysis could potentially serve as a non-invasive, rapid, and reliable method to measure antibiotic concentration and as a surrogate marker for tissue penetration. If combined with smart biosensors, sweat analysis may potentially serve as the first lab-independent, non-invasive antibiotic TDM method.