Medical system, apparatus and method

• US 10,799,185 B2
• Filed: 03/02/2016
• Issued: 10/13/2020
• Est. Priority Date: 05/28/2008
• Status: Active Grant

• Current Assignee: Oridion Medical 1987 Limited (Medtronic Plc)
• Original Assignee: Oridion Medical 1987 Limited (Medtronic Plc)
• Inventors: Colman, Joshua Lewis, Ronen, Michal
• Primary Examiner(s): Starks, Wilbert L
• Application Number:
  US15/058,247
• Publication Number:
  US 20160174907A1
• Time in Patent Office: 1,686 Days
• Field of Search: None
• CPC Class Codes: A61B 10/00 Other methods or instruments for diagnosis, e.g. instruments for taking a cell sample, for biopsy, for vaccination diagnosis vaccination prophylaxis, vaccination therapy A61B17/20; Sex determination; Ovulation-period determination menstruation tables G06C3/00; Throat striking implements
• CPC Class Codes: A61B 2010/0087 Breath samples
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/082 Evaluation by breath analysis, e.g. determination of the chemical composition of exhaled breath A61B5/083, A61B5/091 take precedence
• CPC Class Codes: A61B 5/0833 Measuring rate of oxygen consumption
• CPC Class Codes: A61B 5/0836 Measuring rate of CO2 production
• CPC Class Codes: A61B 5/087 Measuring breath flow
• CPC Class Codes: A61B 5/097 Devices for facilitating collection of breath or for directing breath into or through measuring devices
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• 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 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes 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: A61M 16/0057 Pumps therefor
• CPC Class Codes: A61M 16/0069 the speed thereof being controlled by respiratory parameters, e.g. by inhalation
• CPC Class Codes: A61M 16/0096 : High frequency jet ventilation
• CPC Class Codes: A61M 16/04 : Tracheal tubes catheters in general A61M25/00
• CPC Class Codes: A61M 16/0463 : combined with suction tubes, catheters or the like; Outside connections
• CPC Class Codes: A61M 16/0484 : at the distal end
• CPC Class Codes: A61M 16/0486 : Multi-lumen tracheal tubes
• CPC Class Codes: A61M 16/0488 : Mouthpieces; Means for guiding, securing or introducing the tubes guiding or introducing with laryngoscopes A61B1/267; holding devices on the body A61M25/02
• CPC Class Codes: A61M 16/0816 : Joints or connectors
• CPC Class Codes: A61M 16/085 : Gas sampling
• CPC Class Codes: A61M 16/20 : Valves specially adapted to medical respiratory devices
• CPC Class Codes: A61M 2016/0413 : with detectors of CO2 in exhaled gases
• CPC Class Codes: A61M 2016/103 : the CO2 concentration
• CPC Class Codes: A61M 2230/432 : partial CO2 pressure (P-CO2)
• CPC Class Codes: A61M 5/1723 : using feedback of body parameters, e.g. blood-sugar, pressure measurement of body parameters A61B5/00
• CPC Class Codes: G06N 5/04 : Inference or reasoning models

Abstract

A method of generating a pulmonary index value of a patient, which includes receiving two or more measure patient parameters, wherein at least one of the measured parameters originates from a pulmonary sensor; and computing the pulmonary index value based on the two or more measured patient parameters.

Claims

1. An apparatus for generating a multi-parameter index value indicative of a respiratory condition of a patient, said apparatus comprising:

a port configured to receive two or more non-invasively measured patient parameters, wherein the non-invasively measured patient parameters originate from one or more sensing devices; and

a processor configured to;

(a) characterize a first of the two or more non-invasively measured patient parameter based on a comparison of the first measured patient parameter against a first reference value;

(b) characterize a second of the two or more non-invasively measured patient parameter based on a comparison of the second measured patient parameter against a second reference value; and

(c) compute the multi-parameter index value based on values associated with each of the characterized first and second measured parameters,

wherein the second measured patient parameter is different from the first measured patient parameter;

wherein said processor is configured to average measured patient parameters over a period of time, and wherein the period of time is predetermined or is determined based on one or more patient characteristics, on the stability of one or more measured patient parameters, or both,

wherein said processor is further configured to compute the multi-parameter index value based on the two or more measured patient parameters and on one or more patient characteristics, and

wherein the one or more patient characteristics comprises age, weight, gender, medical condition, medication, ventilation, oxygen supply, lab tests results, blood pressure, medical history, intubation or any combination thereof.

2. The apparatus of claim 1, wherein a weighting factor applied to the value associated with the second characterized measured parameter is influenced by the characterization of the first measured patient parameter.

3. The apparatus of claim 1, wherein said processor is configured to apply a mathematical model reflecting medical expert considerations, literature, clinical data, medical experience or any combination thereof.

4. The apparatus of claim 1, wherein said processor is configured to apply a fuzzy logic, a Bayesian network, a decision tree, a neural network, a radial base function, a linear regression model, a non-linear regression model, an expert system, or any combination thereof.

5. The apparatus of claim 1, wherein the two or more non-invasively measured parameters comprise a CO₂ related parameter and one or more measured parameters selected from the group consisting of:

a CO₂ related parameter respiration rate, an O₂ related parameter, heart rate, an electrocardiogram (ECG), an encephalogram (EEG), blood pressure, and spirometry.

6. The apparatus of claim 5, wherein the CO₂ related parameter comprises a CO₂ waveform related parameter, an expired air CO₂ concentration, respiratory rate or any combination thereof.

7. The apparatus of claim 6, wherein the CO₂ waveform related parameter comprises EtCO₂, changes in EtCO₂, a slope of the increase in the CO₂ concentration, a change in a slope of the increase in the CO₂ concentration, time to rise to a predetermined percentage of a maximum value of CO₂ concentration, a change in time to rise to a predetermined percentage of a maximum value of CO₂ concentration, an angle of rise to a predetermined percentage of a maximum value of CO₂ concentration, a change in an angle of rise to a predetermined percentage of a maximum value of CO₂ concentration, breath to breath correlation, breath to breath correlation, a CO₂ duty cycle, a change in CO₂ duty cycle or any combination thereof.

8. The apparatus of claim 1, wherein said processor is configured to apply a learning process, wherein the learning process increases reliability of the multi-parameter index value.

9. The apparatus of claim 8, wherein the learning process comprises neural network methods, a support vector machine (SVM), genetic algorithms, simulated annealing, expectation-maximization (EM), case based reasoning, or any combination thereof.

10. The apparatus of claim 1, wherein the medical history comprises smoking, heart disease, lung disease, sleep apnea, a pacemaker, or any combination thereof.

11. The apparatus of claim 1, wherein said processor is configured to determine the multi-parameter index-value according to a medical significance of at least one of the parameters.

12. The apparatus of claim 1, wherein said processor is further configured to compute a trend of the multi-parameter index-value.

13. The apparatus of claim 1, wherein said processor is further configured to compute a reliability index of the multi-parameter index-value.

14. The apparatus of claim 1, wherein said processor is further configured to provide a medical recommendation.

15. The apparatus of claim 1, further comprising a graphic display of the multi-parameter index value.

16. The apparatus of claim 1, further comprising a controller configured to adjust at least one parameter related to a patient controlled analgesia (PCA) pump, a dosage management device, a ventilation device or any combination thereof based on the multi-parameter index value.

17. The apparatus of claim 1, wherein the first reference value is representative of a known range and/or pattern of the first non-invasively measured patient parameter and wherein the second reference value is representative of a known range and/or pattern of the second non-invasively measured patient parameter.