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Reliable vital sign assessments are crucial for the management of patients with infectious diseases. Wearable devices enable easy and comfortable continuous monitoring across settings, especially in pediatric patients, but information about their performance in acutely unwell children is scarce. Vital signs were continuously measured with a multi-sensor wearable device (Everion®, Biofourmis, Zurich, Switzerland) in 21 pediatric patients during their hospitalization for appendicitis, osteomyelitis, or septic arthritis to describe acceptance and feasibility and to compare validity and reliability with conventional measurements. Using a wearable device was highly accepted and feasible for health-care workers, parents, and children. There were substantial data gaps in continuous monitoring up to 24 h. The wearable device measured heart rate and oxygen saturation reliably (mean difference, 2.5 bpm and 0.4% SpO2) but underestimated body temperature by 1.7 °C. Data availability was suboptimal during the study period, but a good relationship was determined between wearable device and conventional measurements for heart rate and oxygen saturation. Acceptance and feasibility were high in all study groups. We recommend that wearable devices designed for medical use in children be validated in the targeted population to assure future high-quality continuous vital sign assessments in an easy and non-burdening way. [ABSTRACT FROM AUTHOR]

(1) Background: New cuffless technologies attempting blood-pressure measurements (BPM) offer possibilities to improve hypertension awareness and control. The aim of this study was to compare a smartphone application (app)-based algorithm with office BPM (OBPM). (2) Methods: We included consecutive patients with an indication for ambulatory BPM. The smartphone app (RIVA digital) acquired the pulse wave in the fingers' arterial bed using the phone's camera and estimated BP based on photoplethysmographic (PPG) waveforms. Measurements were alternatingly taken with an oscillometric cuff-based device and smartphone BPM (AppBP) on two consecutive days. AppBP were calibrated to the first OBPM. Each AppBP was compared to its CuffBP (mean of the previous/following OBPM). (3) Results: 50 participants were included, resulting in 50 AppBP values on Day 1 and 33 on Day 2 after exclusion of 225 AppBP due to insufficient quality. The mean ± SD of the differences between AppBP and CuffBP was 0.7 ± 9.4/1.0 ± 4.5 mmHg (p-value 0.739/0.201) on Day 1 and 2.6 ± 8.2/1.3 ± 4.1 mmHg (p-value 0.106/0.091) on Day 2 for systolic/diastolic values, respectively. There were no significant differences between the deviations on Day 1 and Day 2 (p-value 0.297/0.533 for systolic/diastolic values). Overall, there were 10 (12%) systolic measurement pairs differing by >15 mmHg. (4) Conclusions: In this pilot evaluation, the RIVA Digital app shows promising results when compared to oscillometric cuff-based measurements, especially regarding diastolic values. Its differences between AppBP–CuffBP have a good stability one day after calibration. Before clinical use, signal acquisition needs improvement and the algorithm needs to undergo formal validation against a gold-standard BPM method. [ABSTRACT FROM AUTHOR]

A conformational analysis of a stereochemically complete set of peptide analogues based on a cis-enediol unit is presented. The ability of each of the stereoisomers to mimic natural peptides, evaluated by this method, is correlated with its experimental activity in a renin inhibition assay.

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. [ABSTRACT FROM AUTHOR]

Background: Although bedside case presentation contributes to patient-centered care through active patient participation in medical discussions, the complexity of medical information and jargon-induced confusion may cause misunderstandings and patient discomfort.Objective: To compare bedside versus outside the room patient case presentation regarding patients' knowledge about their medical care.Design: Randomized, controlled, parallel-group trial. (ClinicalTrials.gov: NCT03210987).Setting: 3 Swiss teaching hospitals.Patients: Adult medical patients who were hospitalized.Intervention: Patients were randomly assigned to bedside or outside the room case presentation.Measurements: The primary endpoint was patients' average knowledge of 3 dimensions of their medical care (each rated on a visual analogue scale from 0 to 100): understanding their disease, the therapeutic approach being used, and further plans for care.Results: Compared with patients in the outside the room group (n = 443), those in the bedside presentation group (n = 476) reported similar knowledge about their medical care (mean, 79.5 points [SD, 21.6] vs. 79.4 points [SD, 19.8]; adjusted difference, 0.09 points [95% CI, -2.58 to 2.76 points]; P = 0.95). Also, an objective rating of patient knowledge by the study team was similar for the 2 groups, but the bedside presentation group had higher ratings of confusion about medical jargon and uncertainty caused by team discussions. Bedside ward rounds were more efficient (mean, 11.89 minutes per patient [SD, 4.92] vs. 14.14 minutes per patient [SD, 5.65]; adjusted difference, -2.31 minutes [CI, -2.98 to -1.63 minutes]; P < 0.001).Limitation: Only Swiss hospitals and medical patients were included.Conclusion: Compared with outside the room case presentation, bedside case presentation was shorter and resulted in similar patient knowledge, but sensitive topics were more often avoided and patient confusion was higher. Physicians presenting at the bedside need to be skilled in the use of medical language to avoid confusion and misunderstandings.Primary Funding Source: Swiss National Foundation (10531C_ 182422). [ABSTRACT FROM AUTHOR]

Background: The possibility to use built-in smartphone-cameras for photoplethysmographic (PPG) recording of pulse waves lead to the release of numerous health apps, claiming to measure blood pressure (BP) based on PPG signals. Even though these apps are highly popular, not a single one is clinically validated. Aim of the current study was to test systolic BP (sBP) estimation by a promising new algorithm in a large clinical setting.Methods: The study was designed based on the European Society of Hypertension International Protocol Revision 2010. Each individual received 7 sequential BP measurements, starting with the reference device - an automated oscillometric cuff device - followed by the PPG recording at the patients' index finger.Results: A total 1,036 subjects were recruited of which 965 could be included for final analysis leading to 2,895 pairs of comparison. Mean (±SD) error between test and reference device was -0.41 (±16.52) mmHg. Only 38.1% of all 2,895 BP comparisons reached a delta within ±5 mmHg, while 29.3% reached a delta larger than 15 mmHg. Bland-Altman plot showed an overestimation of smartphone sBP in comparison to reference sBP in low range and an underestimation in high sBP range.Conclusions: According to the European Society of Hypertension International Protocol Revision 2010 specifications the algorithm failed validation criteria for sBP measurement and was not commercialized. These findings emphasize that health apps should be rigorously validated according to common guidelines before market release as under- and/or overestimation of BP is potentially exposing persons at health risks in short and long term.Trial Registration: ClinicalTrials.gov, number NCT02552030. [ABSTRACT FROM AUTHOR]

Background:Atrial fibrillation (AF), the most common cardiac arrhythmia, can be detected by smartphones and smartwatches. Introduction:Single-lead ECGs (iECGs) and photoplethysmography (PPG) sensors provide the opportunity for a broad, simple, and easily repeatable cardiac rhythm analysis. To reduce unnecessary medical follow-up testing due to false positive results, our aim was to find a screening approach applicable on smart devices with a focus on high specificity. Methods:We used PPG measurements from smartphones and smartwatches and iECG data from two previous validation trials. Two AF detection algorithms (A and B) were applied on the iECG dataset and compared directly. Further, we used 1-min PPG measurements as a first-pass filter for arrhythmia detection and simulated a sequential testing: Once an arrhythmia was detected in the PPG, the iECG counterpart of the patient was analyzed by algorithm A, B, or A + B combined although algorithm B was primarily designed for PPG analysis. Results:The iECGs from 1,288 participants were analyzed. Algorithm A did not show a diagnosis in 16.1%. In the remaining, sensitivity and specificity were 99.6%, and 97.4% respectively. Accuracy was 98.5%, and correct classification rate (CCR) was 82.7%. Algorithm B always differentiated between normal and arrhythmic and reached an overall sensitivity of 95.4%, a specificity of 91.6%, and an accuracy and CCR of 93.3%. Sequential testing by combining both algorithms into a three-phase test (Test positive PPG, then iECG analysis by A and B combined) resulted in a 100% specificity. Conclusion:Algorithm B performed strongly in PPG analysis as well as iECG analysis. PPG signals and consecutive iECG combined when an arrhythmia was detected by PPG resulted in a specificity that was higher than 99%. Discussion:The analysis allows a direct comparison of iECG algorithms without possible dilution by different measurement procedures or recording-devices. We improved specificity in AF-screening approaches with wearables by simulating a novel approach. Results rely on signal quality. [ABSTRACT FROM AUTHOR]

According to the European Hypertension Guidelines regarding office blood pressure measurements (OBPMs), the mean between second/third or third/fourth OBPM should be taken if the first two readings differ by ≤10 or >10 mmHg, respectively. Our aim was to explore the value of the fourth OBPM and determine whether a simplified OBPM procedure is feasible without loss of quality. In this cross‐sectional study, four standard OBPMs were taken. The mean of the second/third OBPM (S2S3/D2D3) and third/fourth OBPM (S3S4/D3D4) for systolic/diastolic values was calculated. Correlation, agreement, and differences regarding BP classification were explored for the entire cohort and subsets with a difference between the first/second OBPM (S1S2/D1D2) ≤10 and >10 mmHg. Overall (n = 802) and for the subsets with an S1S2 (n = 596) and D1D2 (n = 742) difference ≤10 mmHg, S3S4/D3D4 was in median 0.5 mmHg lower than S2S3/D2D3, respectively (p <.0005 for all). In participants with an S1S2 (n = 206) and D1D2 (n = 60) difference >10 mmHg, S3S4/D3D4 differed numerically from S2S3/D2D3, respectively (p >.1 for all). Overall and for all subsets with an S1S2/D1D2 difference ≤10/>10 mmHg, less subjects were numerically classified as hypertensive with S3S4/D3D4 than with S2S3/D2D3 (p >.04), but BP reclassification occurred in both directions in 1.0%‐10.0%, depending on the cohort. In conclusion, the third/fourth OBPM results in lower BP values than the second/third measurement, regardless of the difference between first/second OBPM, whereby BP reclassifications occurred in both directions. Therefore, the cutoff of >10 versus ≤10mmHg difference between first/second OBPM to implement a fourth BPM harbors the risk of distorted results. We therefore recommend using the second/third BPM for standardized OBPM. Trial registration: Registered on clinicaltrials.gov (NCT02552030). [ABSTRACT FROM AUTHOR]

Aims: Early detection of atrial fibrillation (AF) is essential for stroke prevention. Emerging technologies such as smartphone cameras using photoplethysmography (PPG) and mobile, internet-enabled electrocardiography (iECG) are effective for AF screening. This study compared a PPG-based algorithm against a cardiologist's iECG diagnosis to distinguish between AF and sinus rhythm (SR).Methods and Results: In this prospective, two-centre, international, clinical validation study, we recruited in-house patients with presumed AF and matched controls in SR at two university hospitals in Switzerland and Germany. In each patient, a PPG recording on the index fingertip using a regular smartphone camera followed by iECG was obtained. Photoplethysmography recordings were analysed using an automated algorithm and compared with the blinded cardiologist's iECG diagnosis. Of 672 patients recruited, 80 were excluded mainly due to insufficient PPG/iECG quality, leaving 592 patients (SR: n = 344, AF: n = 248). Based on 5 min of PPG heart rhythm analysis, the algorithm detected AF with a sensitivity of 91.5% (95% confidence interval 85.9-95.4) and specificity of 99.6% (97.8-100). By reducing analysis time to 1 min, sensitivity was reduced to 89.9% (85.5-93.4) and specificity to 99.1% (97.5-99.8). Correctly classified rate was 88.8% for 1-min PPG analysis and dropped to 60.9% when the threshold for the analysed file was set to 5 min of good signal quality.Conclusion: This is the first prospective clinical two-centre study to demonstrate that detection of AF by using a smartphone camera alone is feasible, with high specificity and sensitivity. Photoplethysmography signal analysis appears to be suitable for extended AF screening.Clinical Trial Registration: ClinicalTrials.gov, number NCT02949180, https://clinicaltrials.gov/ct2/show/NCT02949180. [ABSTRACT FROM AUTHOR]

Durch die dynamische Entwicklung von mobilen Sensoren bieten sich Medizinern ständig neue, potentiell kosteneffektive Wege für Diagnostik, Monitoring und Screening. Angetrieben durch den wachsenden Erfolg auf dem Konsumentenmarkt streben immer mehr Wearables & Co in den Gesundheitsmarkt. Weltweit beschäftigen sich Forschungsgruppen seit Jahren mit den Auswirkungen dieser mobilen Technologien auf unser Gesundheitswesen. In der Menge der zahlreichen Möglichkeiten und in Anbetracht der rapiden Entwicklung sollte jedoch bedacht werden, dass für einen erfolgreichen Einsatz am Patienten einige Herausforderungen und Risiken berücksichtigt werden müssen. Der Artikel bietet einen Einstieg in das breite Themenfeld rund um die Einführung von Wearables in die klinische Routine und berichtet über eine Auswahl an realisierten und potentiellen Einsatzmöglichkeiten. Due to the dynamic development of mobile sensors, physicians are continuously provided with new, potentially cost-effective ways for diagnostics, monitoring and screening. Driven by the growing success on the consumer market, more and more wearables & co. are reaching for the healthcare sector and have been the subject of intensive research worldwide for years. With so many opportunities and given the rapid development, it should stay in mind that some challenges and risks must be taken into account for the successful transformation into healthcare products. The article shares considerations from a medical perspective and presents a selection of applications. It shall provide an insight into a broad topic and thus contribute to a better understanding of the whole process around the implementation of wearables into clinical routine. [ABSTRACT FROM AUTHOR]

Aims: Atrial fibrillation (AF) is a progressive arrhythmia characterized by structural alterations that increase its stability. Both clinical and experimental studies showed a concomitant loss of antiarrhythmic drug efficacy in later stages of AF. The mechanisms underlying this loss of efficacy are not well understood. We hypothesized that structural remodelling may explain this reduced efficacy by making the substrate more three-dimensional. To investigate this, we simulated the effect of sodium (Na+)-channel block on AF in a model of progressive transmural uncoupling.Methods and Results: In a computer model consisting of two cross-connected atrial layers, with realistic atrial membrane behaviour, structural remodelling was simulated by reducing the number of connections between the layers. 100% of endo-epicardial connectivity represented a healthy atrium. At various degrees of structural remodelling, we assessed the effect of 60% sodium channel block on AF stability, endo-epicardial electrical activity dissociation (EED), and fibrillatory conduction pattern complexity quantified by number of waves, phase singularities (PSs), and transmural conduction ('breakthrough', BT). Sodium channel block terminated AF in non-remodelled but not in remodelled atria. The temporal excitable gap (EG) and AF cycle length increased at all degrees of remodelling when compared with control. Despite an increase of EED and EG, sodium channel block decreased the incidence of BT because of transmural conduction block. Sodium channel block decreased the number of waves and PSs in normal atrium but not in structurally remodelled atrium.Conclusion: This simple atrial model explains the loss of efficacy of sodium channel blockers in terminating AF in the presence of severe structural remodelling as has been observed experimentally and clinically. Atrial fibrillation termination in atria with moderate structural remodelling in the presence of sodium channel block is caused by reduction of AF complexity. With more severe structural remodelling, sodium channel block fails to promote synchronization of the two layers of the model. [ABSTRACT FROM AUTHOR]

Objectives: Standard operating procedures for office blood pressure measurement (OBPM) vary greatly between guidelines and studies. We aimed to compare the difference between a single OBPM and the mean of the three following measurements. Further, we studied how many patients with possible hypertension may be missed due to short-term masked hypertension (STMH) and how many might be overdiagnosed due to short-term white coat hypertension (STWCH).Design and Setting: In this cross-sectional, single-centre trial, 1000 adult subjects were enrolled. After 5 min of rest, four sequential standard OBPMs were performed at 2 min intervals in a quiet room in sitting position. We compared the first (fBPM) to the mean of the second to fourth measurement (mBPM). STMH was defined as fBPM <140 mm Hg systolic and <90 mm Hg diastolic and mBPM systolic ≥140 mm Hg or diastolic ≥90 mm Hg. STWCH was defined as fBPM systolic ≥140 mm Hg or diastolic ≥90 mm Hg and mBPM <140 mm Hg systolic and <90 mm Hg diastolic.Results: Complete measurements were available in 802 subjects. Between fBPM and mBPM, 662 (82.5%), 441 (55%) and 208 (25.9%) subjects showed a difference in systolic and 531 (66.2%), 247 (30.8%) and 51 (6.4%) in diastolic blood pressure (BP) values of >2 mm Hg, >5 mm Hg and >10 mm Hg, respectively. In 3.4% of initially normotensives STMH and in 34.3% of initially hypertensives, STWCH was apparent.Conclusions: There are significant differences between a single OBPM and the mean of consecutive BP measurements. Our study provides evidence that a single OBPM should not be the preferred method and should be discouraged in future guidelines.Trial Registration Number: NCT02552030;Results. [ABSTRACT FROM AUTHOR]

Hypertensive disorders are one of the leading causes of maternal death worldwide. Several smartphone apps claim to measure blood pressure (BP) using photoplethysmographic signals recorded by smartphone cameras. However, no single app has been validated for this use to date. We aimed to validate a new, promising smartphone algorithm. In this subgroup analysis of the iPARR trial (iPhone App Compared With Standard RR Measurement), we tested the Preventicus BP smartphone algorithm on 32 pregnant women. The trial was conducted based on the European Society of Hypertension International Protocol revision 2010 for validation of BP measuring devices in adults. Each individual received 7 sequential BP measurements starting with the reference device (Omron-HBP-1300) and followed by the smartphone measurement, resulting in 96 BP comparisons. Validation requirements of the European Society of Hypertension International Protocol revision 2010 were not fulfilled. Mean (±SD) systolic BP disagreement between the test and reference devices was 5.0 (±14.5) mm Hg. The number of absolute differences between test and reference device within 5, 10, and 15 mm Hg was 31, 53, and 64 of 96, respectively. A Bland-Altman plot showed an overestimation of smartphone-determined systolic BP in comparison with reference systolic BP in low range but an underestimation in medium-range BP. The Preventicus BP smartphone algorithm failed the accuracy criteria for estimating BP in pregnant women and was thus not commercialized. Pregnant women should be discouraged from using BP smartphone apps, unless there are algorithms specifically validated according to common protocols.Clinical Trial Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT02552030. [ABSTRACT FROM AUTHOR]