Abgeschlossene klinische Studien
Wissenschaftliche klinische Studien sind die unabdingbare Voraussetzung für ein verlässliches Medizinprodukt. Vor allem dann, wenn es dabei um ein so außerordentlich wichtiges Thema wie den Herzrhythmus geht.
Im Folgenden stellen wir die wichtigsten Ergebnisse klinischer Studien zu Preventicus Heartbeats zusammen. In den jüngsten internationalen Studien (DETECT AF PRO und WATCH AF) wurde an über 1.300 Studienteilnehmern gezeigt, dass Vorhofflimmern mit einer Genauigkeit von ca. 96% erkannt wurde.
Der positive Vorhersagewert war bei Smartphonemessungen über 99% (das heißt: bei durch Preventicus Heartbeats erkanntem Vorhofflimmern wurde dieses nachher in über 99% der Fälle durch einen Kardiologen per EKG bestätigt).
It is the world’s first international, prospective, double-blinded and multicenter clinical trial (ClinicalTrials.gov ID: NCT02956343) evaluating the accuracy of a smartwatch to detect atrial fibrillation. Preventicus provided the application for blinded analysis of 1 minute plethysmographic raw data (recorded with pulse sensor of Samsung Gear Fit II smartwatch) of 636 study participants. Total accuracy of the application was 96.2% with a positive predictive value of 97.4% in comparison to the ECG based diagnosis of two cardiologists.
Noé Brasier, Christina J Raichle, Marcus Dörr, Adrian Becke, Vivien Nohturfft, Stefan Weber, Fabienne Bulacher, Lorena Salomon, Thierry Noah, Ralf Birkemeyer, Jens Eckstein. Detection of atrial fibrillation with a smartphone camera: first prospective, international, two-centre, clinical validation study (DETECT AF PRO). EP Europace, , euy176, https://doi.org/10.1093/europace/euy176
ClinicalTrials.gov Identifier: NCT02270112
The AF detection algorithm available in the smartphone camera application (“Preventicus Heartbeats”) has been tested in the DETECT AF PRO trial (ClinicalTrials.gov ID: NCT02949180); a prospective, double-blinded, international multicenter study with 672 participants. Total accuracy of the smartphone camera application to detect AF was 96.5% with a positive predictive value of 99.3% compared to the ECG based diagnosis of two cardiologists by mutual agreement.
Karim N, Aral S et al. AF detection using smartphone apps. EP Europace, Volume 19, Issue suppl_3, 1 June 2017, Pages iii234 https://doi.org/10.1093/ehjci/eux151.246
To determine whether smartphone apps that require no additional peripheral devices are sufficiently accurate to screen populations for atrial fibrillation (AF), the authors prospectively and blinded analyzed smartphone apps, e.g. Preventicus Heartbeats utilizing the camera function in comparison to cardiologists AF diagnosis based on ECG. 140 in- and outpatients recruited with AF and sinus rhythm (n=70, 73±12 mean age, 60% male and n=70, 59±20, 66% respectively) were administered a 12-lead ECG along with a questionnaire on their smartphone use. The sensitivity and specificity of Preventicus was 94% and 96% respectively.
Weber S, Eckstein J et al. Detection of premature beats using smartphone-based pulse wave recordings. EP Europace, Volume 19, Issue suppl_3, 1 June 2017, Pages iii347 https://doi.org/10.1093/ehjci/eux158.243
To detect premature beats when they actually occur, the authors aimed to determine how accurate this is possible with pulse waves captured with a smartphone camera. ECG and pulse wave recordings were taken in 46 patients simultaneously. In total 17.416 beats have been analyzed and classified by ECG based decision of a cardiologist: 16.875 regular beats and 541 irregular (premature beats. Detection of premature beats with the pulse wave algorithm achieved an overall accuracy of 99.5% (sensitivity 91.2%, specificity 99.7%) compared to cardiologists decision based on ECG.
Krivoshei L, Weber S et al. Smart detection of atrial fibrillation. EP Europace, Volume 19, Issue 5, 1 May 2017.
A novel app using the camera sensor of an iPhone 4S with integrated LED, the researchers tested pulse wave signals acquired from the fingertips of 80 patients with atrial fibrillation (AF) (n=40) and in sinus rhythm (SR) (n=40). They aimed to test the app’s accuracy in detecting AF compared to cardiologists AF decision based on ECG. Conclusion: app reliably discriminated between SR and AF, best parameter set achieved an overall accuracy of 95% (sensitivity 95%, specificity 95%).
Koenig N, Seeck A et al. Validation of a new heart rate measurement algorithm for fingertip recording of video signals with smartphones. Telemed J E Health. 2016 Aug;22(8):631-6. https://www.ncbi.nlm.nih.gov/pubmed/26938673
This German and Swiss team of investigators used an algorithm they developed to measure heart rate (HR). Detection of the steepest slope of every pulse wave is combined and correlated to an optimized pulse wave pattern. In 68 subjects, the pulse curves were recorded as video signals with a Apple iPhone 4S and simultaneously by ECG. The smartphone-measured HR proved extremely consistent (R > 0.99) with ECGs. Most standard linear HRV parameters also showed high correlations of R ≥ 0.90. Overall, HR and HRV captured by smartphone as video signals with the algorithm proved accurate for preclinical cardiac screening.