iPhone App compared with standard blood pressure measurement -The iPARR trial.

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.

Comparison and Combination of Single-Lead ECG and Photoplethysmography Algorithms for Wearable-Based Atrial Fibrillation Screening.

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.

Detection of atrial fibrillation with a smartphone camera: first prospective, international, two-centre, clinical validation study (DETECT AF PRO).

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.

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.

The WATCH AF Trial: SmartWATCHes for Detection of Atrial Fibrillation.

OBJECTIVES: The WATCH AF (SmartWATCHes for Detection of Atrial Fibrillation) trial compared the diagnostic accuracy to detect atrial fibrillation (AF) by a smartwatch-based algorithm using photoplethysmographic (PPG) signals with cardiologists' diagnosis by electrocardiography (ECG). BACKGROUND: Timely detection of AF is crucial for stroke prevention. METHODS: In this prospective, 2-center, case-control trial, a PPG pulse wave recording using a commercially available smartwatch was obtained along with Internet-enabled mobile ECG in 672 hospitalized subjects. PPG recordings were analyzed by a novel automated algorithm. Cardiologists' diagnoses were available for 650 subjects, although 142 (21.8%) datasets were not suitable for PPG analysis, among them 101 (15.1%) that were also not interpretable by the automated Internet-enabled mobile ECG algorithm, resulting in a sample size of 508 subjects (mean age 76.4 years, 225 women, 237 with AF) for the main analyses. RESULTS: For the PPG algorithm, we found a sensitivity of 93.7% (95% confidence interval [CI]: 89.8% to 96.4%), a specificity of 98.2% (95% CI: 95.8% to 99.4%), and 96.1% accuracy (95% CI: 94.0% to 97.5%) to detect AF. CONCLUSIONS: The results of the WATCH AF trial suggest that detection of AF using a commercially available smartwatch is in principle feasible, with very high diagnostic accuracy. Applicability of the tested algorithm is currently limited by a high dropout rate as a result of insufficient signal quality. Thus, achieving sufficient signal quality remains challenging, but real-time signal quality checks are expected to improve signal quality. Whether smartwatches may be useful complementary tools for convenient long-term AF screening in selected at-risk patients must be evaluated in larger population-based samples. (SmartWATCHes for Detection of Atrial Fibrillation [WATCH AF]:; NCT02956343).

Performance of a Blood Pressure Smartphone App in Pregnant Women: The iPARR Trial (iPhone App Compared With Standard RR Measurement).

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.