Medical system, apparatus and method

• US 9,307,944 B2
• Filed: 03/18/2013
• Issued: 04/12/2016
• Est. Priority Date: 05/28/2008
• Status: Active Grant

• Current Assignee: Oridion Medical (1987) Ltd. (Medtronic Plc)
• Original Assignee: Oridion Medical 1987 Limited (Medtronic Plc)
• Inventors: Colman, Joshua Lewis, Ronen, Michal
• Primary Examiner(s): Starks, Wilbert L
• Application Number:
  US13/845,454
• Publication Number:
  US 20130289364A1
• Time in Patent Office: 1,121 Days
• Field of Search: None
• US Class Current: 1/1
• 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

There is provided a method of generating a pulmonary index value of a patient, which includes receiving two or more measured 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. A method of computing a multi-parameter index value indicative of a pulmonary condition of a patient, said method comprising:

receiving two or more non-invasively measured patient parameters, wherein the two or more non-invasively measured patient parameters originating from one or more sensing devices, the two or more non-invasively measured patient parameters including 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, spirometry, or any combination thereof, and the CO₂ related parameter is obtained from a breath sample;

characterizing a first of said two or more non-invasively measured patient parameters based on a comparison of the first non-invasively measured patient parameter against a first reference value, the first reference value is representative of a known range and/or pattern of the first non-invasively measured patient parameter;

characterizing a second of said two or more non-invasively measured patient parameter based on a comparison of the second non-invasively measured patient parameter against a second reference value, the second reference value is representative of a known range and/or pattern of the second non-invasively measured patient parameter and the second non-invasively measured patient parameter is different from the first non-invasively measured patient parameter; and

computing the index value based on an integrative analysis of values associated with each of the characterized first and second non-invasively measured patient parameters, wherein a weighting factor applied to the value associated with the second characterized non-invasively measured parameter is influenced by the characterization of the first non-invasively measured patient parameter.

2. The method of claim 1, wherein computing the index comprises applying a mathematical model reflecting medical expert considerations, literature, clinical data, medical experience or any combination thereof.

3. The method of claim 1, wherein computing the index comprises applying 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.

4. The method of claim 1, wherein the measured patient parameters are averaged over a period of time.

5. The method of claim 4, wherein the period of time is predetermined.

6. The method of claim 4, wherein the period of time is determined based on one or more patient characteristics, on the stability of one or more measured patient parameters, or both.

7. The method of claim 1, wherein the CO₂ related parameter comprises a CO₂ waveform related parameter, an expired air CO₂ concentration, respiratory rate or any combination thereof.

8. The method of claim 7, 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, a change in breath to breath correlation, a CO₂ duty cycle, a change in CO₂ duty cycle, or any combination thereof.

9. The method of claim 1, further comprising applying a learning process, wherein the learning process increases reliability of the index value.

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

11. The method of claim 1, further comprising receiving one or more patient characteristics;

and

computing the index value based on the two or more measured patient parameters and on one or more patient characteristics.

12. The method of claim 11, 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.

13. The method of claim 12, wherein the medical history comprises smoking, heart disease, lung disease, sleep apnea, a pacemaker, or any combination thereof.

14. The method of claim 1, wherein the index-value is in the range of 1 to 10.

15. The method of claim 1, wherein an increase in the index-value is indicative of an improvement in the condition of the patient.

16. The method of claim 1, wherein a decrease in the index-value is indicative of a deterioration in the condition of the patient.

17. The method of claim 1, wherein computing the index value further comprises taking into account a medical significance of at least one of the parameters.

18. The method of claim 1, further comprising computing a trend of the index-value.

19. The method of claim 1, further comprising computing a reliability index of the index-value.

20. The method of claim 1, further comprising providing a medical recommendation.

21. The method of claim 1, further comprising adjusting 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 index value.

22. The method of claim 1, further comprising displaying the index value on a graphic display.

23. The method of claim 1, wherein the index value is a pulmonary index value.

24. The method of claim 1, wherein the index value is linearly proportional to a severity of the pulmonary condition.