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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: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]

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]