Arytenoid prolapse and inability to ventilate

Tracheal sounds accurately detect apnea in patients recovering from anesthesia

Apnea should be monitored continuously in the post anesthesia care unit (PACU) to avoid serious complications. It has been confirmed that tracheal sounds can be used to detect apnea during sedation in healthy subjects, but the performance of this acoustic method has not been evaluated in patients with frequent apnea events in the PACU. Tracheal sounds were acquired from the patients in the PACU using a microphone encased in a plastic bell. Concurrently, a processed nasal pressure signal was used as a reference standard to identify real respiratory events. The logarithm of the tracheal sound variance (log-var) was used to detect apnea, and the results were compared to the reference method. Sensitivity, specificity, positive likelihood ratios (PLR), and negative likelihood ratios (NLR) were calculated. One hundred and twenty-one patients aged 55.5 ± 13.2 years (mean ± SD) with a body mass index of 24.6 ± 3.7 kg/m² were included in data analysis. The total monitoring time was 52.6 h. Thirty-four patients experienced 236 events of apnea lasting for a total of 122.2 min. The log-var apnea detection algorithm detected apnea with 92% sensitivity, 98% specificity, 46 PLR and 0.08 NLR. The performance of apnea detection in the PACU using the log-var tracheal sounds method proved to be reliable and accurate. Tracheal sounds could be used to minimize the potential risks from apnea in PACU patients.

The effect of dental scaling noise during intravenous sedation on acoustic respiration rate (RRa™)

BACKGROUND

Respiration monitoring is necessary during sedation for dental treatment. Recently, acoustic respiration rate (RRa™), an acoustics-based respiration monitoring method, has been used in addition to auscultation or capnography. The accuracy of this method may be compromised in an environment with excessive noise. This study evaluated whether noise from the ultrasonic scaler affects the performance of RRa in respiratory rate measurement.

METHODS

We analyzed data from 49 volunteers who underwent scaling under intravenous sedation. Clinical tests were divided into preparation, sedation, and scaling periods; respiratory rate was measured at 2-s intervals for 3 min in each period. Missing values ratios of the RRa during each period were measuerd; correlation analysis and Bland-Altman analysis were performed on respiratory rates measured by RRa and capnogram.

RESULTS

Respective missing values ratio from RRa were 5.62%, 8.03%, and 23.95% in the preparation, sedation, and scaling periods, indicating an increased missing values ratio in the scaling period (P < 0.001). Correlation coefficients of the respiratory rate, measured with two different methods, were 0.692, 0.677, and 0.562 in each respective period. Mean capnography-RRa biases in Bland-Altman analyses were -0.03, -0.27, and -0.61 in each respective period (P < 0.001); limits of agreement were -4.84-4.45, -4.89-4.15, and -6.18-4.95 (P < 0.001).

CONCLUSIONS

The probability of missing respiratory rate values was higher during scaling when RRa was used for measurement. Therefore, the use of RRa alone for respiration monitoring during ultrasonic scaling may not be safe.

Comparison of Postoperative Respiratory Monitoring by Acoustic and Transthoracic Impedance Technologies in Pediatric Patients at Risk of Respiratory Depression

BACKGROUND

In children, postoperative respiratory rate (RR) monitoring by transthoracic impedance (TI), capnography, and manual counting has limitations. The rainbow acoustic monitor (RAM) measures continuous RR noninvasively by a different methodology. Our primary aim was to compare the degree of agreement and accuracy of RR measurements as determined by RAM and TI to that of manual counting. Secondary aims include tolerance and analysis of alarm events.

METHODS

Sixty-two children (2-16 years old) were admitted after tonsillectomy or receiving postoperative patient/parental-controlled analgesia. RR was measured at regular intervals by RAM, TI, and manual count. Each TI or RAM alarm resulted in a clinical evaluation to categorize as a true or false alarm. To assess accuracy and degree of agreement of RR measured by RAM or TI compared with manual counting, a Bland-Altman analysis was utilized showing the average difference and the limits of agreement. Sensitivity and specificity of RR alarms by TI and RAM are presented.

RESULTS

Fifty-eight posttonsillectomy children and 4 patient/parental-controlled analgesia users aged 6.5 ± 3.4 years and weighting 35.3 ± 22.7 kg (body mass index percentile 76.6 ± 30.8) were included. The average monitoring time per patient was 15.9 ± 4.8 hours. RAM was tolerated 87% of the total monitoring time. The manual RR count was significantly different from TI (P = .007) with an average difference ± SD of 1.39 ± 10.6 but were not significantly different from RAM (P = .81) with an average difference ± SD of 0.17 ± 6.8. The proportion of time when RR measurements differed by ≥4 breaths was 22% by TI and was 11% by RAM. Overall, 276 alarms were detected (mean alarms/patient = 4.5). The mean number of alarms per patient were 1.58 ± 2.49 and 2.87 ± 4.32 for RAM and TI, respectively. The mean number of false alarms was 0.18 ± 0.71 for RAM and 1.00 ± 2.78 for TI. The RAM was found to have 46.6% sensitivity (95% confidence interval [CI], 0.29-0.64), 95.9% specificity (95% CI, 0.90-1.00), 88.9% positive predictive value (95% CI, 0.73-1.00), and 72.1% negative predictive value (95% CI, 0.61-0.84), whereas the TI monitor had 68.5% sensitivity (95% CI, 0.53-0.84), 72.0% specificity (95% CI, 0.60-0.84), 59.0% positive (95% CI, 0.44-0.74), and 79.5% negative predictive value (95% CI, 0.69-0.90).

CONCLUSIONS

In children at risk of postoperative respiratory depression, RR assessment by RAM was not different to manual counting. RAM was well tolerated, had a lower incidence of false alarms, and had better specificity and positive predictive value than TI. Rigorous evaluation of the negative predictive value is essential to determine the role of postoperative respiratory monitoring with RAM.