Affiliation:
1. Department of Cardiology, Reinier de Graaf Hospital , Reinier de Graafweg 5, 2625 AD Delft , The Netherlands
2. Corsano Health B.V. , Wilhelmina van Pruisenweg 35, 2595 AN The Hague , The Netherlands
Abstract
Abstract
Aims
Elevated blood pressure (BP) is a key risk factor in cardiovascular diseases. However, obtaining reliable and reproducible BP remains a challenge. This study, therefore, aimed to evaluate a novel cuffless wristband, based on photoplethysmography (PPG), for continuous BP monitoring.
Methods and results
Predictions by a PPG-guided algorithm were compared to arterial BP measurements (in the sub-clavian artery), obtained during cardiac catheterization. Eligible patients were included and screened based on AAMI/European Society of Hypertension (ESH)/ISO Universal Standard requirements. The machine learning-based BP algorithm required three cuff-based initialization measurements in combination with ∼100 features (signal-derived and patient demographic-based). Ninety-seven patients and 420 samples were included. Mean age, weight, and height were 67.1 years (SD 11.1), 83.4 kg (SD 16.1), and 174 cm (SD 10), respectively. Systolic BP was ≤100 mmHg in 48 samples (11%) and ≥160 mmHg in 106 samples (25%). Diastolic BP was ≤70 mmHg in 222 samples (53%) and ≥85 mmHg in 99 samples (24%). The algorithm showed mean errors of ±3.7 mmHg (SD 4.4 mmHg) and ±2.5 mmHg (SD 3.7 mmHg) for systolic and diastolic BP, respectively. Similar results were observed across all genders and skin colours (Fitzpatrick I-VI).
Conclusion
This study provides initial evidence for the accuracy of a PPG-based BP algorithm in combination with a cuffless wristband across a range of BP distributions. This research complies with the AAMI/ESH/ISO Universal Standard, however, further research is required to evaluate the algorithms performance in light of the remaining European Society of Hypertension recommendations.
Clinical trial registration
www.clinicaltrials.gov, NCT05566886.
Publisher
Oxford University Press (OUP)