FLUID ANALYZER AND ASSOCIATED METHODS

• US 20140323906A1
• Filed: 03/14/2014
• Published: 10/30/2014
• Est. Priority Date: 03/15/2013
• Status: Active Grant

• Current Assignee: Atrium Medical Corporation (Getinge AB)
• Original Assignee: Atrium Medical Corporation (Getinge AB)
• Inventors: Peatfield, Greg, Karwoski, Ted
• Granted Patent: US 10,201,315 B2
• Time in Patent Office: Days
• US Class Current: 600/561
• CPC Class Codes: A61B 5/036 by means introduced into body tracts A61B5/032 and A61B5/035 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 5/4848 Monitoring or testing the effects of treatment, e.g. of medication
• CPC Class Codes: A61B 5/6852 Catheters
• CPC Class Codes: A61B 5/7225 Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation A61B5/30, A61B5/316 take precedence; transformers for specific applications H01F38/00; amplifiers H03F
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/7475 User input or interface means, e.g. keyboard, pointing device, joystick
• CPC Class Codes: A61M 1/69 Drainage containers not being adapted for subjection to vacuum, e.g. bags devices worn by the patient for reception of urine A61F5/44
• CPC Class Codes: A61M 1/74 Suction control underwater drainage A61M1/61
• CPC Class Codes: A61M 1/84 Drainage tubes; Aspiration tips for negative pressure wound therapy A61M1/90; for surgical cutting instruments A61B17/32
• CPC Class Codes: A61M 2205/3334 Measuring or controlling the flow rate
• CPC Class Codes: A61M 2205/3344 Measuring or controlling pressure at the body treatment site
• CPC Class Codes: A61M 2205/3389 Continuous level detection A61M2205/3393 takes precedence
• CPC Class Codes: A61M 2205/52 with memories providing a history of measured variating parameters of apparatus or patient
• CPC Class Codes: A61M 2210/101 Pleural cavity
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

Devices, computer readable programs and methods determine a patient parameter, including volume and/or flow rate of a fluid draining through a drain tube from a chest cavity of a patient, by using at least one pressure value at an end of the drain tube associated with a fluid collection canister and at least one pressure value within the drain tube at a location distant from the collection canister. The pressure values are processed with a non-linear solver to determine the patient parameter.

Claims

1. A method for determining at least one patient parameter in a patient having a drain tube having a first end and a second end, said first end fitted into the patient chest cavity and a second end in fluid communication with a collection vessel, at least one of said collection vessel and said drain tube having a first pressure sensor sensing the pressure within at least one of (i) said drain tube in a location proximal to the collection vessel, and (ii) the collection vessel, and a second pressure sensor sensing the pressure within the drain tube at a location distal to the collection vessel, said method comprising the steps of:

a. obtaining at least a first pressure value from said first pressure sensor and at least one first pressure value from said second pressure sensor; and

,

b. calculating at least one patient parameter by the pressure values through a non-linear solver.

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18. A computer readable program for determining at least one patient parameter, in a patient having said drain tube having a first end and a second end, said first end fitted into and/or establishing fluid communication with a patient'"'"'s chest cavity and a second end establishing fluidic communication with a collection vessel, at least one of the collection vessel and said drain tube having a first pressure sensor sensing the pressure within at least one of (i) said drain tube in a location proximal to the collection vessel and (ii) said collection vessel, and a second pressure sensor sensing the pressure within the drain tube at a location distal to the collection vessel, said computer readable program comprising instructions for a computer means having memory to:

a. obtain a first pressure value from said first pressure sensor and a first pressure value from said second pressure sensor; and

b. calculate at least one patient parameter by the pressure values through a non-linear solver.

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31. The computer readable program of claim 24 wherein said non-linear equation is expressed as:

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33. A device for determining at least one patient parameter, in a patient having a drain tube having a first end and a second end, said first end fitted into and/or establishing fluid communication with the patient'"'"'s chest cavity, and a second end received in a collection vessel, said device comprising:

a. a first pressure sensor constructed and arranged to sense the pressure within at least one of (i) the drain tube in a location proximal to the collection vessel and (ii) said collection vessel;

b. a second pressure sensor constructed and arranged to sense the pressure within the drain tube at a location distal to the collection vessel;

c. said first pressure sensor and said second pressure sensor constructed and arranged for being placed in signal communication with computing means having memory for;

i. obtaining a first pressure value from said first pressure sensor and a first pressure value from said second pressure sensor; and

ii calculating at least one patient parameter by the pressure values through a non-linear solver.

34. The device of claim 33, wherein said at least one first pressure values comprise a plurality of pressure values.

35. The device of claim 34, wherein said plurality of pressure values are made at timed intervals.

36. The device of claim 35, further comprising computer means having memory.

37. The device of claim 36, wherein said plurality of pressure values are stored in computer memory.

38. The device of claim 36, wherein said computer means produces an output displaying said at least one patient parameter.

39. The device of claim 36, wherein said computer means calculates said at least one patient parameter by a non-linear equation.

40. The device of claim 39, wherein said non-linear equation is implemented in one or more approaches selected from the group consisting of Support Vector Machine Approach, Artificial Neural Networks, Genetic Algorithms and Genetic Programming.

41. The device of claim 39, wherein said non-linear equation is the linear regression hypothesis:

42. The device of claim 41, wherein said linear regression equation has a cost function J expressed with training set of “

m”

values, expressed as follows;

43. The device of claim 42, wherein unknown “

Theta”

values are determined with training set of “

n”

items as follows;

44. The device of claim 41, wherein said non-linear equation and its derivatives are solved by Gradient Descent, Particle Swarm Optimization and Artificial Bee Colony approaches.

45. The device of claim 43, wherein Theta is derived as follows:

46. The device of claim 39, wherein said non-linear equation is expressed as:

47. The device of claim 33, wherein said fluid is a biological fluid comprising any one of the group consisting of gas, water, serum, blood, plasma, lymph, pus therapeutic infusion fluids and mixtures thereof.

48. The device of claim 33, further comprising a collection vessel.

49. The device of claim 33, further comprising a drain tube.

50. The device of claim 36, wherein said computer means monitors at least one additional patient parameter selected from the group consisting of error in volume data, error in flow data, filter clogging, breathing status, drain tube clogging, liquid in lines, and excessive fluid movements.

53. The device of claim 33, wherein the patient parameter is an estimation of liquid fluid collected in a collection vessel.

56. The device of claim 33, wherein said non-linear solver is trained.

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