Wearable Wireless Respiratory Monitoring System That Detects and Predicts Opioid Induced Respiratory Depression
Trial ID: NCT07217197
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-01-20
Completion date: 2026-11-30
Sponsor: Thomas Jefferson University
Collaborators: RTM Vital Signs, LLC; National Institute on Drug Abuse (NIDA)
Conditions: Respiration Disorders
Interventions: Study to evaluate a Respiratory Monitoring System (RMS) with a Tracheal Sound Sensor (TSS) for detecting and predicting opioid induced respiratory Depression (OIRD).
Locations: Thomas Jefferson University Hospital
Countries: United States
Enrollment: 120
Age range: 18 Years - None
Sex: ALL

Summary:
This study is being conducted to evaluate the ability of the Respiratory Monitoring System (RMS) to detect and predict opioid induced respiratory depression (OIRD) in post-operative surgical patients managed with opioid medications. The ability of the RMS to detect OIRD will be compared to the detection of OIRD using a commercial capnometer, pulse oximeter, airflow monitor, and breathing volume monitor .

We hypothesize the RMS will detect the onset and progression of a true OIRD event with high sensitivity, specificity, positive predictive value, and negative predictive value. A true OIRD event will be determined by the reference device trend data.

RTM Vital Signs, LLC is developing a Respiratory Monitoring System (RMS) that consists of a wearable Trachea Sound Sensor (TSS) and a software application that measures the sounds of air flow within the trachea during inhalation and exhalation and cardiovascular sounds. The sounds of airflow in the trachea are used to continuously monitor a patient's respiratory rate (RR), relative tidal volume (TV), relative minute ventilation (MV), pattern of breathing, duration of apnea in a healthcare setting. The cardiovascular sounds are used to continuously monitor pulse rate and PR variability.

Once commercialized, clinicians will observe the RMS trend data on a smart phone, bedside display, or electronic medical record to determine whether the patient is breathing within their normal range, breathing more than their normal range (hyperventilation), breathing less than their normal range (hypoventilation), or not breathing (apnea). Real-time alerts and alarms will be based upon trends in a patient's rate and depth of breathing, number and duration of apnea events, RTM's Risk-Index-Score, and RTM's machine learning/artificial intelligence methods.

Eligibility criteria:
Inclusion Criteria:

1. Surgical patients routinely managed post-op with opioid medication as their primary analgesic for break through pain.
2. Age ≥ 18 years
3. BMI 20 to 40
4. American Society of Anesthesiologists physical status I, II, and III.
5. Understands written and spoken English language

Exclusion Criteria:

1. Age \< 18 years.
2. BMI \< 20 or \> 40.
3. American Society of Anesthesiologists physical status IV and V.
4. Active Do Not Resuscitate (DNR) order.
5. Does not understand written and spoken English well.
6. Anxiety or claustrophobia related to wearing a face mask or nasal cannula.
7. History of skin irritation or inflammation related to the adhesives or materials used in the TSS sensor, facemask, nasal cannula, or pulse oximeter probe.
8. Active infection or inflammation of the skin above the proximal trachea.
9. Anticipated hospital length of stay less than 24 hours.
10. Excessive facial hair that may prevent attachment of the RMS sensor
11. Unstable cardiovascular or pulmonary function or any condition that, in the opinion of the Investigator, would interfere with their participation in the trial or pose an excessive risk to study staff (e.g., known history of hepatitis B or C).
12. Pregnancy or breast feeding.
13. Current participation in an industry sponsored pharmaceutical study or a medical device study.

Outcomes:
Evaluate RMS Trend Data Prior to Each True OIRD Event to Determine the Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive Value for Detecting and Predicting a True OIRD Event.