SYSTEMS AND METHODS FOR PREDICTING ADVERSE EVENTS AND ASSESSING LEVEL OF SEDATION DURING MEDICAL PROCEDURES

• US 20170042475A1
• Filed: 08/12/2016
• Published: 02/16/2017
• Est. Priority Date: 03/22/2013
• Status: Active Grant

• Current Assignee: Children's Medical Center Corporation, Massachusetts Institute of Technology
• Original Assignee: Children's Medical Center Corporation, Massachusetts Institute of Technology
• Inventors: Verghese, George Cheeran, Guo, Margaret Gan, Mieloszyk, Rebecca, Heldt, Thomas, Krauss, Baruch Shlomo
• Granted Patent: US 10,388,405 B2
• Time in Patent Office: Days
• CPC Class Codes: A61B 5/0836 Measuring rate of CO2 production
• CPC Class Codes: A61B 5/4821 Determining level or depth of anaesthesia A61B5/1106 takes precedence
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7271 Specific aspects of physiological measurement analysis A61B5/316, A61B5/349 and A61B5/374 take precedence
• CPC Class Codes: A61B 5/746 Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: G16C 20/30 Prediction of properties of chemical compounds, compositions or mixtures
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

Systems and methods are disclosed herein for quantitatively identifying a patient'"'"'s sedation level and predicting adverse events, based on one or more capnograms or outputs from a pharmacokinetic, pharmacodynamic, or ventilatory model. A sensor measures a carbon dioxide concentration of air exhaled by a patient into a breath receiver. A processor processes the sensor data to generate a capnogram including one or more respiratory cycles, computes the outputs of pharmacokinetic, pharmacodynamic, or ventilatory models, and extracts one or more of the resulting features from the capnogram and pharmacokinetic, pharmacodynamic, or ventilatory model outputs. A multi-parameter metric is computed based on the one or more extracted features and estimates the current or predicted sedation level of the patient.

Claims

1. A system for automatically providing a quantitative assessment of a physiological state of a patient during procedural sedation, comprising:

a breath receiver in fluid communication with a patient undergoing procedural sedation;

a sensor for measuring a carbon dioxide concentration in air captured by the breath receiver, the sensor being coupled to the breath receiver; and

a processor configured to;

process data from the sensor to generate, in real time, a capnogram associated with the patient, the capnogram including one or more respiratory cycles;

extract, in real time, one or more features from the capnogram that are indicative of the physiological state of the patient;

compute, in real time, a metric indicative of the physiological state of the patient based on the one or more features from the capnogram;

compute a degree of confidence in the physiological state indicated by the metric;

determine a baseline value of the metric for the patient, the baseline value corresponding to a baseline state of the patient before procedural sedation begins; and

monitor, in real time, a value of the metric relative to the baseline value and an associated physiological state.

2. The system of claim 1, wherein the processor is further configured to:

detect in real time a change in the value of the metric over at least two respiratory cycles; and

determine in real time a change in the physiological state of the patient based on the change in the value of the metric.

3. The system of claim 2, wherein the processor is further configured to:

correlate the physiological state of the patient with one or more physiological data or indicators to determine an accuracy of the determined change in physiological state.

4. The system of claim 3, wherein the one or more physiological data or indicators are input to a clustering technique and include at least one of physiological data provided by a user, outputs from at least one of a pharmacokinetic, pharmacodynamic, and ventilatory model, and a score on a qualitative sedation scoring scheme.

5. The system of claim 1, wherein the extracting the one or more features includes fitting a portion of the capnogram to a parameterized function.

6. The system of claim 1, wherein the one or more features include a measure of periodicity of the capnogram.

7. The system of claim 1, wherein the one or more features include the output of at least one of a pharmacokinetic model, a pharmacodynamics model, and a ventilatory model.

8. The system of claim 1, wherein the one or more features that are indicative of the physiological state of the patient include a terminal value of CO2 on exhalation, an end-exhalation slope, and a ratio of an intermediate exhalation slope over an initial exhalation slope.

9. The system of claim 1, wherein the processor is further configured to use, in real time, a clustering technique to determine clusters of the one or more features indicative of the physiological state of the patient.

10. The system of claim 9, wherein the clustering technique is a k-means technique, with a number “

k”

of clusters corresponding to a number of sedation states for the patient.

11. The system of claim 9, wherein the clustering technique is a technique with a variable number of clusters.

12. The system of claim 9, wherein the metric is a multi-parameter metric, where the multi-parameter metric indicates a separation from a cluster centroid.

13. The system of claim 12, wherein a closest centroid, as determined by the multi-parameter metric, is indicative of the physiological state of the patient.

14. The system of claim 12, wherein a separation from a nearest centroid relative to a separation from a next-closest centroid is indicative of a degree of confidence in the physiological state of the patient.

15. The system of claim 1, wherein the physiological state of the patient pertains to a sedation level.

16. A system for guiding procedural sedation, comprising at least one processor configured to:

identify sedation agent information including at least one of a time, a type, and an amount of sedation agent administered to a patient;

compute, using a pharmacokinetic model, a concentration of sedation agent in the body of the patient based on the sedation agent information;

compute a first predicted sedation level based on the computed concentration;

select a candidate dose of sedation agent based on the sedation agent information;

compute a second predicted sedation level based on the candidate dose of sedation agent; and

provide, to a display, at least one of the computed concentration and the first predicted sedation level and at least one of the candidate dose of sedation agent and the second predicted sedation level.

17. The system of claim 16, wherein the at least one processor is further configured to select a pharmacodynamics model, wherein the pharmacodynamic model is used to estimate an effect resulting from the computed concentration.

18. The system of claim 17, wherein the at least one processor is further configured to compute the first predicted sedation level based on the computed concentration and the pharmacodynamics model.

19. The system of claim 17, wherein the pharmacokinetic model and the pharmacodynamic model are compartmental models.

20. The system of claim 16, wherein the at least one processor is further configured to alert a user when the computed concentration exceeds a first concentration threshold or is below a second concentration threshold.

21. The system of claim 16, wherein the at least one processor is further configured to alert a user when the first predicted sedation level exceeds a first sedation threshold or is below a second sedation threshold.

22. The system of claim 16, wherein the system further comprises an interactive bedside monitor configured to record sedation agent information.

23. The system of claim 16, wherein the pharmacokinetic model includes parameters based on at least one of age, weight, height, lean body mass, gender, and procedure type.

24. The system of claim 16, wherein the computed concentration comprises at least one of a plasma concentration and an effect-site concentration.

25. The system of claim 16, wherein alerting the user when the computed concentration exceeds a first concentration threshold or is below a second concentration threshold is based on an emergence threshold of the sedation agent.

26. The system of claim 16, wherein the display continuously updates the graphic presentation of the computed concentration.

27. A method for automatically providing a quantitative assessment of a physiological state of a patient during procedural sedation, comprising:

receiving, at a processor, from a sensor, data indicating a carbon dioxide concentration in air captured by a breath receiver, the sensor being coupled to the breath receiver;

processing data from the sensor to generate, in real time, a capnogram associated with the patient, the capnogram including one or more respiratory cycles;

extracting, in real time, one or more features from the capnogram that are indicative of a physiological state of the patient;

computing, in real time, a metric indicative of a physiological state of the patient based on the one or more features from the capnogram;

computing a degree of confidence in the physiological state indicated by the metric;

determining a baseline value of the metric for the patient, the baseline value corresponding to a baseline state of the patient before procedural sedation begins; and

monitoring, in real time, a value of the metric relative to the baseline value and an associated physiological state.

28. A method for automatically guiding procedural sedation, comprising:

identifying sedation agent information including at least one of a time, a type, and an amount of sedation agent administered to a patient;

computing, using a pharmacokinetic model, a concentration of sedation agent in the body of the patient based on the sedation agent information;

computing a first predicted sedation level based on the computed concentration;

selecting a candidate dose of sedation agent based on the sedation agent information;

computing a second predicted sedation level based on the candidate dose of sedation agent; and

providing, to a display, at least one of the computed concentration and the first predicted sedation level and at least one of the candidate dose of sedation agent and the second predicted sedation level.