Abstract Rationale High-flow oxygen therapy (HFOT) is widely used for hypoxemic respiratory failure. Respiratory rate (RR) is a key physiologic indicator guiding escalation or de-escalation of therapy, yet continuous bedside monitoring is often unreliable or impractical. We evaluated a novel RR sensor integrated into a commercial HFOT system, designed to derive RR directly from airflow signals. This approach aims to eliminate the need for external sensors, reduce costs, and provide real-time continuous respiratory monitoring. Methods A two-part study evaluated the sensor’s accuracy. Initially, Benchtop testing used a high-fidelity lung simulator (ASL 5000) across three patient models (normal adult, COPD, ARDS) and four HFOT flow rates (10, 30, 50, 60 L/min). HFOT Sensor-derived RR was compared with the simulator’s reference RR signal. Thereafter, Human volunteer testing included five healthy adults at five flow rates (10, 30, 50, 60, 70 L/min) performing various breathing patterns. RR obtained from the HFOT system (Bonhawa, Telesair) was compared with respiratory rate derived by capnography (CMI Health, PC-900B). The primary endpoint was agreement between the RR measured by the integrated sensor and the reference method. Agreement was assessed using Bland-Altman analysis, calculating mean bias and 95% limits of agreement (LoA). A priori clinical acceptance was defined as ± 2 breaths per minute (BPM). Analyses were stratified by flow rate. Results Across simulated and human testing, the integrated RR sensor demonstrated strong agreement with reference measurements. Mean bias remained within ±2 BPM for most conditions. Higher flow rates introduced minor variability without clinically meaningful drift. Accuracy was consistent across simulated disease states and among volunteers. Conclusion The novel RR sensor integrated into a HFOT system accurately measured respiratory rate across diverse flow conditions. Embedding RR monitoring within the therapy device simplifies workflow, enables continuous monitoring, and may reduce reliance on external sensors. These results support further clinical validation of integrated physiologic sensors to enhance the safety and efficiency of HFOT therapy. This abstract is funded by: Telesair Inc
Tunnell et al. (Fri,) studied this question.