Comparison of 7 Different Sensors for Detecting Low Respiratory Rates Using a Single Breath Detection Algorithm in Nonintubated, Sedated Volunteers. (August 2019)

• Record Type: Journal Article
• Authors: Ermer, Sean; Brewer, Lara; Orr, Joe; Egan, Talmage D.; Johnson, Ken
• Is Part Of: Anesthesia & analgesia. Volume 129:Number 2(2019)
• Publication Date: 2019-08
• Subjects: Anesthesiology -- Periodicals; Anesthesia; Anesthesiology; Analgesia; Analgesics; Anesthesiology -- Periodicals; 617.9605
• DOI: 10.1213/ANE.0000000000003793
• Languages: English
• ISSNs: 0003-2999

Abstract

Abstract : BACKGROUND: Numerous technologies are used to monitor respiratory rates in nonintubated patients. No technology has emerged as the standard. The primary aim of this study was to assess the limits of agreement between a reference sensor signal (respiratory inductance plethysmography bands) and 7 alternative sensor signals (nasal capnometer, nasal pressure transducer, oronasal thermistor, abdominal accelerometer, transpulmonary electrical impedance, peritracheal microphone, and photoplethysmography) for measuring low respiratory rates in sedated, nonintubated, supine volunteers. A unified approach based on a single breath detection algorithm was applied to each sensor to facilitate comparison. We hypothesized that all of the sensor signals would allow detection of low (<10 breaths per minute) respiratory rates to within ±2 breaths per minute of the reference sensor signal. METHODS: Volunteers received remifentanil and propofol infusions at selected target concentration pairs to induce depression of ventilation. Signals from each sensor were analyzed by an identical threshold-based detection algorithm to compute the breathing rate. Bland-Altman limits of agreement and error rate analyses were used to characterize the performance of each sensor compared to the reference sensor. RESULTS: The analysis of the accelerometer and capnometer signals, using Bland-Altman and error rate analyses, showed the highest breath rate agreement (1.96 × standard deviation) of the 7 sensors with −2.1 to 2.2 and −2.5 to 2.7 breaths per minute, respectively. All other signals exhibited wider limits of agreement, with impedance being the widest at −7.8 to 7.4 breaths per minute. For the abdomen accelerometer, 95% of Bland-Altman data points were within ±2 breaths per minute. For the capnometer, 96% of data points were within ±2 breaths per minute. Nasal pressure, thermistor, and microphone all had >80% of data points within ±2 breaths per minute. Impedance and photoplethysmograph signals had 58% and 64%, respectively. CONCLUSIONS: A unified approach can be applied to a variety of sensor signals to estimate respiratory rates in spontaneously breathing, nonintubated, sedated volunteers. However, detecting clinically relevant low respiratory rates (<6 breaths per minute) is a technical challenge. By our analysis, no single sensor was able to detect slow respiratory rates with adequate precision (<±2 breaths per minute of the reference signal). Of the sensors evaluated, capnometers and abdominal accelerometers may be the most reliable sensors for identifying hypopnea and central apnea.