Apparatus and methods for computing cardiac output of a living subject via applanation tonometry

• US 9,949,696 B2
• Filed: 03/14/2013
• Issued: 04/24/2018
• Est. Priority Date: 03/14/2013
• Status: Active Grant

• Current Assignee: Zhejiang Shanshi Biological Medical DevicesCompany Limited
• Original Assignee: Tensys Medical Inc. (Recorders & Medicare Systems Pvt Ltd.)
• Inventors: Goedje, Oliver, Bohn, Matthias, Hunziker, Patrick, Morozov, Oleksii, Friedrich, Felix
• Primary Examiner(s): D ABREU, MICHAEL JOSEPH
• Application Number:
  US13/829,061
• Publication Number:
  US 20140275937A1
• Time in Patent Office: 1,867 Days
• CPC Class Codes: A61B 3/165 Non-contacting tonometers
• CPC Class Codes: A61B 5/02 Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/02108 from analysis of pulse wave characteristics
• 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 5/028 by thermo-dilution
• CPC Class Codes: A61B 5/029 Measuring or recording blood output from the heart, e.g. minute volume A61B8/065 takes precedence
• CPC Class Codes: A61B 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 8/02 Measuring pulse or heart rate
• CPC Class Codes: A61B 8/06 Measuring blood flow measuring volume flow in general G01F, e.g. G01F1/66, G01F1/72; measuring speed of fluids in general G01P5/00
• CPC Class Codes: A61B 8/065 to determine blood output from the heart in general A61B5/029
• CPC Class Codes: A61B 8/12 in body cavities or body tracts, e.g. by using catheters catheters per se A61M25/00
• CPC Class Codes: A61B 8/4416 related to combined acquisition of different diagnostic modalities, e.g. combination of ultrasound and X-ray acquisitions
• CPC Class Codes: A61B 8/481 involving the use of contrast agent, e.g. microbubbles introduced into the bloodstream analysis of echo signal exploiting properties of a contrast enhancer in devices for short-range imaging with acoustic waves G01S7/52039
• CPC Class Codes: A61B 8/488 involving Doppler signals

Abstract

Apparatus and methods for calculating cardiac output (CO) of a living subject using applanation tonometry measurements. In one embodiment, the apparatus and methods build a nonlinear mathematical model to correlate physiologic source data vectors to target CO values. The source data vectors include one or more measurable or derivable parameters such as: systolic and diastolic pressure, pulse pressure, beat-to-beat interval, mean arterial pressure, maximal slope of the pressure rise during systole, the area under systolic part of the pulse pressure wave, gender (male or female), age, height and weight. The target CO values are acquired using various methods, across a plurality of individuals. Multidimensional nonlinear optimization is then used to find a mathematical model which transforms the source data to the target CO data. The model is then applied to an individual by acquiring physiologic data for the individual and applying the model to the collected data.

Claims

1. A method for computing cardiac output from tonometry data using a clinical operator device, said method comprising:

measuring one or more hemodynamic parameter values of a living test subject using applanation of a tonometric sensor in data communication with said clinical operator device;

processing said one or more hemodynamic parameter values of said living test subject using a pre-determined mathematical model to determine a cardiac output value of said living test subject, said pre-determined mathematical model stored in a non-transitory storage medium of said clinical operator device, said processing being carried out substantially automatically via a computerized process, said processing comprising;

automatically inputting said measured one or more hemodynamic parameter values into said pre-determined mathematical model;

calculating, via said pre-determined mathematical model, a test subject input vector for said living test subject from said measured one or more hemodynamic parameter values of said living test subject; and

correlating, via said pre-determined mathematical model, said test subject input vector to one of a plurality of linked input vectors and cardiac output target values to determine said cardiac output value of said living test subject from said one or more hemodynamic parameter values; and

based at least in part on said processing, automatically causing display on a display device of said cardiac output value for said living test subject,

wherein said pre-determined mathematical model is previously generated by;

measuring one or more physiologic parameter values from each of a plurality of model living subjects;

generating a plurality of input vectors, each of said plurality of input vectors based at least in part on said one or more measured physiologic parameter values for one of said plurality of model living subjects;

collecting a cardiac output measurement from each of said plurality of model living subjects, said cardiac output measurement determined via one or more non-tonometry data-based cardiac output determination techniques;

computing a plurality of cardiac output target values based at least on said collected cardiac output measurement from each of said plurality of model living subjects;

linking, utilizing multidimensional non-linear optimization, a corresponding one of said plurality of input vectors to one of said plurality of computed cardiac output target values for each of said plurality of model living subjects to generate said plurality of linked input vectors and cardiac output target values; and

storing said plurality of linked input vectors and cardiac output target values.

2. The method of claim 1, wherein each of said plurality of input vectors comprises one or more of:

said one or more measured physiologic parameter values, one or more entered physiologic parameter values, or one or more derived physiologic parameter values from each of said plurality of model living subjects.

3. The method of claim 2, wherein said one or more measured physiologic parameter values comprises one or more of:

a height, a weight, a blood pressure parameter, or a beat-to-beat interval (BTBI) for each of said plurality of living test model subjects.

4. The method of claim 2, wherein said one or more entered physiologic parameter values comprise one or more of:

an age or a gender of each of said plurality of model living subjects.

5. The method of claim 2, wherein said one or more derived physiologic parameter values comprise one or more of:

a maximal slope within a systole or a systolic area for each of said plurality of model living subjects.

6. The method of claim 1, wherein said hemodynamic parameter values comprise one or more of:

a pressure parameter, a beat-to-beat interval, or an arterial compliance of said living test subject.

7. The method of claim 1, wherein said cardiac output measurement of each of said plurality of model living subjects is previously obtained by one or more of:

oxygen consumption of blood, dilution of injected indicator, ultrasound, echocardiography, transcutaneous Doppler technique, transesophageal Doppler technique, impedance cardiography, electrical cardiography, or magnetic resonance imaging (MRI).

8. The method of claim 1, wherein said pre-determined mathematical model is generated by an algorithm derivation apparatus and is transferred from said algorithm derivation apparatus to said clinical operator device.

9. The method of claim 8, wherein said plurality of target cardiac output values is determined by at least:

collecting a cardiac output measurement from each of said plurality of model living subjects; and

computing said plurality of target cardiac output values based at least on said collected cardiac output measurements.

10. The method of claim 1, wherein said generation of said pre-determined mathematical model comprises machine learning.

11. The method of claim 1, further comprising:

identifying a position of a blood vessel of interest of said living test subject via analysis of backscattered acoustic energy via said tonometric sensor, said tonometric sensor comprising a pressure transducer and an ultrasonic transducer; and

based at least in part on said identifying, positioning said tonometric sensor over said blood vessel of interest;

wherein said measuring of said one or more hemodynamic parameter values of said living test subject using applanation of said tonometric sensor comprises measuring of said one or more hemodynamic parameter values of said living test subject using applanation of said tonometric sensor at said blood vessel of interest.

12. A method for computing cardiac output from tonometry data using a cardiac output calculation device, said method comprising:

measuring one or more hemodynamic parameter values of a living test subject using applanation of a tonometric sensor in data communication with said cardiac output calculation device;

automatically processing said measured one or more hemodynamic parameter values using a pre-determined mathematical model to determine a cardiac output value of said living test subject, said pre-determined mathematical model stored in a non-transitory storage medium of said cardiac output calculation device, said pre-determined mathematical model comprising a multidimensional non-linear optimization algorithm derived from one or more previously obtained physiologic parameter values correlated via non-linear optimization with at least two previously obtained cardiac output measurements for each of a plurality of model living subjects to generate a plurality of linked input vectors and cardiac output target values, said processing comprising;

inputting said measured one or more hemodynamic parameter values into said pre-determined mathematical model;

calculating, via said pre-determined mathematical model, a test subject input vector for said living test subject from said measured one or more hemodynamic parameter values of said living test subject; and

correlating, via said pre-determined mathematical model, said test subject input vector to one of said plurality of linked input vectors and cardiac output target values in order to determine said cardiac output value of said living test subject; and

in response to said processing, causing display on a display device of a cardiac output value for said living test subject based on a result of said processing of said measured one or more hemodynamic parameter values using said pre-determined mathematical model.

13. The method of claim 12, wherein said one or more hemodynamic parameter values comprise one or more of:

a pressure parameter, a beat-to-beat interval, or an arterial compliance of said living test subject.

14. The method of claim 12, wherein said mathematical model is generated by at least:

measuring said one or more physiologic parameter values from each of said plurality of model living subjects;

generating a plurality of input vectors, each of said plurality of input vectors representing said measured one or more physiologic parameter values for one of said plurality of model living subjects;

collecting said at least two cardiac output measurements from each of said plurality of model living subjects;

computing a plurality of cardiac output target values based at least on said collected cardiac output measurement from each of said plurality of model living subjects; and

linking, utilizing said non-linear optimization, a corresponding one of said plurality of input vectors to one of said plurality of computed cardiac output target values for each of said plurality of model living subjects to generate said plurality of linked input vectors and cardiac output target values; and

storing said plurality of linked input vectors and cardiac output target values.

15. The method of claim 14, wherein said pre-determined mathematical model is configured to correlate said obtained one or more hemodynamic parameter values of said living test subject to one of said plurality of linked input vectors and cardiac output target values.

16. The method of claim 14, further comprising calculating a test input vector for said living test subject from said obtained one or more hemodynamic parameter values of said living test subject, and

correlating said test input vector to one of said plurality of linked input vectors and cardiac output target values via said pre-determined mathematical model.

17. The method of claim 12, wherein said multidimensional non-linear optimization algorithm comprises a Gauss-Newton algorithm.

18. A computer readable apparatus comprising a storage medium configured to store at least one computer program thereon, said at least one computer program comprising a plurality of instructions which are configured to, when executed by a processing apparatus of a cardiac output calculation device:

measure one or more hemodynamic parameter values of a living test subject via applanation of a tonometric sensor in data communication with said cardiac output calculation device;

automatically input said measured one or more hemodynamic parameter values into a pre-determined mathematical model stored on said storage medium;

calculate, via said pre-determined mathematical model, a test subject input vector for said living test subject from said measured one or more hemodynamic parameter values of said living test subject;

correlate, via said pre-determined mathematical model, said test subject input vector to at least one of a linked plurality of input vectors and cardiac output target values to determine said cardiac output value of said living test subject, said pre-determined mathematical model generated at least in part by application of a multidimensional non-linear optimization algorithm on a plurality of input vectors and a plurality of cardiac output measurements to produce said linked plurality of input vectors and cardiac output target values, each of said plurality of input vectors and said plurality of cardiac output measurement previously calculated from data collected from a plurality of model living subjects; and

cause display of said cardiac output value for said living test subject on a display device in data communication with said processing apparatus.

19. The apparatus of claim 18, wherein said pre-determined mathematical model is previously generated by an algorithm derivation apparatus and is transferred from said algorithm derivation apparatus to said clinical operator device.

20. The apparatus of claim 18, wherein said pre-determined mathematical model is previously generated by:

measurement one or more physiologic parameter values from each of said plurality of model living subjects;

generation of said plurality of input vectors, each of said plurality of input vectors based at least in part on said one or more measured physiologic parameter values for one of said plurality of model living subjects;

collection of said plurality of cardiac output measurements, said plurality of cardiac output measurements comprising at least one cardiac output measurement from each of said plurality of model living subjects;

computation of a plurality of cardiac output target values based at least on said at least one collected cardiac output measurement from each of said plurality of living model subjects;

linkage, via said application of said multidimensional non-linear optimization algorithm, of a corresponding one of said plurality of input vectors to one of said plurality of computed cardiac output target values for each of said plurality of model living subjects to generate said linked plurality of input vectors and cardiac output target values; and

storage of said linked plurality of input vectors and cardiac output target values.

21. The apparatus of claim 20, wherein said at least one cardiac output measurement from each of said plurality of living subjects comprises at least two cardiac output measurements from each of said plurality of living subjects, each of said at least two cardiac output measurements measured via a non-arterial pressure-based cardiac output determination technique.