Procedural Sedation Monitoring: Systems and Methods for Predicting Adverse Events and Assesing Level of Sedation
This invention is a system for monitoring sedation state and detecting adverse events during procedural sedation. The system can be implemented in a standalone monitor or incorporated into commercially available monitoring systems within clinical settings.
Researchers
-
systems and methods for predicting adverse events and assessing level of sedation during medical procedures
United States of America | Granted | 10,388,405 -
systems and methods for predicting adverse events and assessing level of sedation during medical procedures
United States of America | Granted | 11,972,843
Figures
Fig. 1: The capnogram collected from a cardioversion patient is shown with detected exhalation segments highlighted. Also marked are four features extracted from the capnogram exhalation segments. These features are etco2 and slope features S 1, s2, and s3.
Fig. 2: Causal clustering of the capnogram recording from a 75-year-old woman undergoing cardioversion. The three features used in clustering are plotted in the first three panes. Black vertical markers correspond to propofol administrations, the green marker aligns with the start of the cardioversion procedure, and the red marker indicates the end of the procedure. State assignments following causal k-means clustering are shown in the fourth pan
Fig. 3: Centroid movement during causal clustering of the same cardioversion patient's exhalations analyzed in fig. 2. One set of three centroids is found for each exhalation using information from only the current and past exhalations. The centroid triangles evolve from the light-colored centroid triangles found at the beginning of the recording to the dark-colored centroid triangles determined at the end.
Technology
Following the 2011 guidelines of the American Society of Anesthesiologists, capnography, a noninvasive measurement of exhaled carbon dioxide, has become the standard of care for ventilation monitoring of sedated patients. This invention quantitatively identifies a patient's sedation level and predicts adverse events based on information from the capnogram and outputs from pharmacokinetic, pharmacodynamics or ventilatory models. This system is comprised of a breath receiver, a sensor and a processor. The sensor measures the carbon dioxide concentration of air exhaled by the patient into the breath receiver. The processor processes the sensor data to generate capnograms, and computes the outputs of pharmacokinetic, pharmacodynamic and ventilatory models. The processor also extracts the resulting features from these models and a multi-parameter metric is computed based on the extracted features to determine the current or predicted sedation level of the patient. Assessing an individual's ventilatory state during sedation provides a unique perspective on patient's clinical status. In particular, parameter extraction and predictive methods derived from capnography and other physiological data can improve the safety and operational effectiveness of the sedation.
Problems Addressed
Procedural sedation has allowed many painful procedures to be conducted outside the operating room. During such procedures, it is important to maintain an appropriate level of sedation to minimize the risk of respiratory depression (if the patients are over-sedated) or pain or anxiety (if under-sedated). However, there are no objective, data-driven means to measure a patient's evolving level of sedation during a procedure. Currently, clinicians use light physical or verbal stimulation to gauge the degree of the patient's responsiveness as a surrogate measure of sedation level. Furthermore, the current patient safety monitoring can detect but not predict adverse events. Therefore, despite the high level of surveillance offered by the standard monitoring systems in place today, adverse respiratory events, such as apnea and hypoxemia, frequently occur.
Advantages
- Respiratory-focused patient monitoring system
- Helps reduce subjectivity from clinical decision making with respect to individual sedation and respiratory state
- Predicts adverse respiratory events
Publications
Mieloszyk, R. J., et al. "Clustering of Capnogram Features to Track State Transitions During Procedural Sedation." In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 1699-1702. doi: 10.1109/EMBC.2015.7318704.
Hardesty, L. "Diagnostic Exhalations." MIT News, November 6, 2014.
License this technology
Interested in this technology? Connect with our experienced licensing team to initiate the process.
Sign up for technology updates
Sign up now to receive the latest updates on cutting-edge technologies and innovations.