Wearable Wireless Respiratory Monitoring System That Detects and Predicts Opioid Induced Respiratory Depression
NCT ID: NCT06442488
Last Updated: 2025-10-15
Study Results
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Basic Information
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COMPLETED
14 participants
OBSERVATIONAL
2024-05-01
2024-09-30
Brief Summary
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Detailed Description
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Proprietary machine learning/AI algorithms convert the sounds of airflow into the measurements of respiratory rate (RR), tidal volume (TV), minute ventilation (MV), and duration of apnea. Sensor information is transmitted to a bedside PC that displays the vital sign data in real-time. The wearable, wireless RMS is being developed for hospital and outpatient use as a tool to detect and predict respiratory compromise/clinical deterioration in a more-timely and accurately manor (fewer false alerts/alarms) than current methods.
The breathing data from 14 to 20 participants will be recorded during one study session lasting approximately 90 minutes with the sensor/cradle adhered to the neck over the proximal trachea. Reference breathing data will be recorded simultaneously using a hospital ventilator's pneumotach and capnometer attached to a tight-fitting face mask.
Each subject will be instructed to breath the following protocol 3 or 4 times:
Record RMS data and pneumotach/capnometer data for \~400 seconds with the study subject breathing a normal RR and TV.
Record RMS data and pneumotach/capnometer data for \~70 seconds with the study subject breathing a normal RR and an increased TV.
Record RMS data and pneumotach/capnometer data for \~70 seconds with the study subject breathing a normal RR and decreased TV.
Record RMS data and pneumotach/capnometer data for \~120 seconds with the study subject breathing a normal RR and normal TV with a period of apnea in the middle (15 seconds).
Record RMS data and pneumotach/capnometer data for \~120 seconds with the study subject breathing a normal RR and decreased TV, with a period of apnea in the middle (15 seconds).
Record RMS data and pneumotach/capnometer data for \~120 seconds with the study subject breathing a decreased RR and decrease TV with a period of apnea in the middle (15 seconds).
RMS data will be compared to reference pneumotach/capnometer data (RR, TV, MV, and duration of apnea) to determine the accuracy of measurement. Data will be recorded in an environment with simulated hospital noise with adaptive filtering and active noise cancellation turned on and turned off.
This observational human study will compare the signal-to-noise ratio (SNR) and the measurement accuracy of the RMS in a noisy environment with the adaptive filtering and active noise cancellation turned on versus turned off.
Participants will be contacted by telephone 3 to 4 days later to confirm no adverse effects from the study methods or wearing the sensor.
Conditions
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Study Design
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COHORT
PROSPECTIVE
Interventions
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Respiratory Monitoring System
Comparing the SNR and accuracy of measurement (RR, TV, MV, apnea duration) in a noisy external environment when the RMS has adaptive filtering and active noise cancellation turned on versus turned off.
Eligibility Criteria
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Inclusion Criteria
2. BMI 20 to 38.
3. Subject understands the English language, understands the risks, benefits, and alternatives to this research study, and is willing and able to give written informed consent.
Exclusion Criteria
2. BMI \< 20 or \> 38.
3. Does not understand written and spoken English.
4. Anxiety or claustrophobia related to wearing a face mask.
5. History of skin irritation or inflammation related to the adhesive, adhesive tape, or materials used in the trachea sound sensor or facemask.
6. Active infection or inflammation of the skin above the proximal trachea.
7. Excessive facial hair that may prevent a tight seal around the facemask.
8. Unstable cardiac, vascular, pulmonary, hepatic, renal, immune function at the discretion of the investigator.
9. Pregnancy or breast feeding.
10. Current participation in an industry sponsored pharmaceutical study or a medical device study.
18 Years
70 Years
ALL
Yes
Sponsors
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RTM Vital Signs, LLC
INDUSTRY
Jeffrey Joseph
OTHER
Responsible Party
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Jeffrey Joseph
Professor of Anesthesiology
Locations
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Thomas Jefferson University
Philadelphia, Pennsylvania, United States
Countries
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References
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Yu L, Ting CK, Hill BE, Orr JA, Brewer LM, Johnson KB, Egan TD, Westenskow DR. Using the entropy of tracheal sounds to detect apnea during sedation in healthy nonobese volunteers. Anesthesiology. 2013 Jun;118(6):1341-9. doi: 10.1097/ALN.0b013e318289bb30.
Chen G, de la Cruz I, Rodriguez-Villegas E. Automatic lung tidal volumes estimation from tracheal sounds. Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:1497-500. doi: 10.1109/EMBC.2014.6943885.
Thakor NV, Zhu YS. Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection. IEEE Trans Biomed Eng. 1991 Aug;38(8):785-94. doi: 10.1109/10.83591.
Ramsay MA, Usman M, Lagow E, Mendoza M, Untalan E, De Vol E. The accuracy, precision and reliability of measuring ventilatory rate and detecting ventilatory pause by rainbow acoustic monitoring and capnometry. Anesth Analg. 2013 Jul;117(1):69-75. doi: 10.1213/ANE.0b013e318290c798. Epub 2013 Apr 30.
Harper VP, Pasterkamp H, Kiyokawa H, Wodicka GR. Modeling and measurement of flow effects on tracheal sounds. IEEE Trans Biomed Eng. 2003 Jan;50(1):1-10. doi: 10.1109/TBME.2002.807327.
Patino M, Kalin M, Griffin A, Minhajuddin A, Ding L, Williams T, Ishman S, Mahmoud M, Kurth CD, Szmuk P. Comparison of Postoperative Respiratory Monitoring by Acoustic and Transthoracic Impedance Technologies in Pediatric Patients at Risk of Respiratory Depression. Anesth Analg. 2017 Jun;124(6):1937-1942. doi: 10.1213/ANE.0000000000002062.
Other Identifiers
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