EP-4739203-A1 - SYSTEMS AND METHODS TO PREDICT GLOBAL HYPOPERFUSION IN CRITICAL CARE PATIENTS

Abstract

A system for determining a global hypoperfusion index (GHI) includes an arterial blood pressure sensor, a ventricular blood pressure sensor, an oximetry module, and an integrated hardware unit including a system processor and a system memory. The system memory includes instructions that cause the system to receive arterial hemodynamic data, ventricular hemodynamic data, and blood oxygen saturation data. One or more right ventricular pressure features, one or more pulmonary artery pressure features, one or more cardiac output parameters, and one or more venous oxygen saturation parameters are derived from the arterial hemodynamic data, the ventricular hemodynamic data, and the blood oxygen saturation data. The GHI is derived by using a predictive decision model based upon the one or more right ventricular pressure features, the one or more pulmonary artery features, the one or more cardiac output parameters, and the one or more venous oxygen saturation parameters.

Inventors

• MOSES, Kevin, James
• POTES BLANDON, Cristhian, M.
• LEE, CHRISTINE

Assignees

• Becton, Dickinson and Company

Dates

Publication Date

20260513

Application Date

20240725

Claims (20)

1. 1. A system for determining a global hypoperfusion index (GHI) representative of a prediction of a future global hypoperfusion event within a patient, the system comprising: an arterial blood pressure sensor including a first housing, a first fluid input port connected via tubing to a first fluid source, a first catheter-side fluid port connected to a catheter inserted within an arterial system of a patient, first pressure transducer in communication with the first fluid source through the first catheter-side fluid port, and a first I/O cable in electrical communication with the first pressure transducer; a ventricular blood pressure sensor including a second housing, a second fluid input port connected via tubing to a second fluid source, a second catheterside fluid port connected to the catheter inserted within a ventricular system of a patient, second pressure transducer in communication with the second fluid source through the second catheter-side fluid port, and a second I/O cable in electrical communication with the second pressure transducer; an oximetry module including an optical transmitter, an optical receiver, and an I/O cable in electrical communication with the optical transmitter and the optical receiver; an integrate hardware unit including: a system processor; a system memory; a display including a user interface; and an analog-to-digital (ADC) converter; wherein the system memory includes instructions that, when executed by the system processor, cause the system to perform the following steps: receive arterial hemodynamic data from the arterial blood pressure sensor; receive ventricular hemodynamic data from the ventricular blood pressure sensor; receive blood oxygen saturation data from the oximetry module; derive, using a first algorithm, one or more right ventricular pressure features from the ventricular hemodynamic data; derive, using a second algorithm, one or more pulmonary artery pressure features from the arterial hemodynamic data; derive, using a third algorithm, one or more cardiac output parameters from the ventricular hemodynamic data and/or the arterial hemodynamic data; derive, using a fourth algorithm, one or more venous oxygen saturation parameters from the blood oxygen saturation data; derive the GHI by using a predictive decision model based upon the one or more right ventricular pressure features, the one or more pulmonary artery features, the one or more cardiac output parameters, and the one or more venous oxygen saturation parameters; and display the GHI on the hemodynamic display.
2. 2. The system of claim 1 , wherein the first algorithm is a right ventricular pressure algorithm that is applied to a right ventricular pressure waveform from the ventricular hemodynamic data to derive the one or more right ventricular pressure features, and wherein the one or more right ventricular pressure features include one or more of: a pulse rate, a maximum pressure rate of change with respect to time (“dP/dt”) during systolic rise, a minimum dP/dt during a relaxation period after end systole, a systole time, a systolic pressure, an end systolic pressure, an end diastolic pressure, a pulse pressure, and a mean pressure.
3. 3. The system of claim 1, wherein the second algorithm is a pulmonary artery pressure (PAP) algorithm, wherein the PAP algorithm is applied to a pulmonary artery pressure waveform from the arterial hemodynamic data to derive the one or more pulmonary artery pressure features, and wherein the one or more pulmonary artery features include a mean pulmonary artery pressure.
4. 4. The system of claim 1 , wherein the third algorithm is a right ventricular cardiac output (RVCO) algorithm, wherein the RVCO algorithm is applied to a right ventricular pressure (RVP) waveform from the ventricular hemodynamic data and/or the arterial hemodynamic data to derive the one or more cardiac output parameters, and wherein the cardiac output parameters include a continuous cardiac output.
5. 5. The system of claim 4, wherein the RVCO algorithm includes applying a validation module to the RVP waveform to determine if the RVP waveform is valid.
6. 6. The system of claim 4, wherein the RVCO algorithm includes converting the right ventricular pressure waveform of the patient into a waveform of blood flow of the patient via a machine learning model, and wherein the RVCO algorithm includes filtering the waveform of blood flow to remove artifacts or physiological inaccuracies to yield a processed blood flow waveform.
7. 7. The system of claim 1 , wherein the RVCO algorithm is applied to a right ventricular pressure (RVP) waveform, a pulmonary artery pressure (PAP) waveform, and an intermittent cardiac output from the arterial hemodynamic data and/or the ventricular hemodynamic data to derive the one or more cardiac output parameters.
8. 8. The system of claim 7, wherein the RVCO algorithm includes applying a validation module to the RVP waveform and to the PAP waveform to determine if the RVP waveform and the PAP waveform are valid.
9. 9. The system of claim 1 , wherein the fourth algorithm is a venous oxygen saturation algorithm, wherein the venous oxygen saturation algorithm is used to derive a venous oxygen saturation value and a signal quality index from the blood oxygen saturation data.
10. 10. The system of claim 1 , wherein the system memory is further encoded with instructions that, when executed by the system processor, cause the system to: display a graph of change in the venous oxygen saturation value over time on the hemodynamic display.
11. 11. The system of claim 1 , wherein: the predictive decision model includes a machine learning model, a feature creation module, and a model heuristics module; the machine learning model is a predictive risk model based upon a linear, weighted set of predictive features that have been identified as being predictive of a global hypoperfusion event, wherein the set of predictive features predictive of the global hypoperfusion event are identified using a regression model that minimizes loss; and the feature creation module computes one or more intermediate features based upon the one or more right ventricular pressure features, the one or more cardiac output parameters, the one or more pulmonary artery pressure features, and the one or more venous oxygen saturation parameters, wherein the one or more intermediate features comprise arterial elastance and pulmonary vascular resistance.
12. 12. The system of claim 1 , wherein system memory is further encoded with instructions that, when executed by the system processor, cause the system to: assign a risk level to the global hypoperfusion index based on one or more predetermined thresholds, wherein the risk level is indicative of a patient condition based upon the global hypoperfusion index; and output a sensory alarm when the risk level indicates that a patient assessment is required, wherein the risk level indicating that patient assessment is recommended is output when a predicted venous oxygen saturation value is below 60.
13. 13. The system of claim 12, wherein the system memory is further encoded with instructions that, when executed by the system processor, cause the system to: provide secondary screening information indicative of one or more possible causes of the patient condition when the risk level indicates that a patient assessment is required.
14. 14. The system of claim 1 , wherein the third algorithm is an Arterial Pressure Cardiac Output (APCO) algorithm.
15. 15. A method for determining a global hypoperfusion index (GHI) representative of a prediction of a future global hypoperfusion event within a patient, the method comprising: receiving arterial hemodynamic data from an arterial blood pressure sensor; receiving ventricular hemodynamic data from a ventricular blood pressure sensor; receive blood oxygen saturation data from an oximetry module; deriving, using a right ventricular pressure algorithm, one or more right ventricular pressure features from the ventricular hemodynamic data; deriving, using a pulmonary artery pressure (PAP) algorithm, one or more pulmonary artery pressure features from the arterial hemodynamic data; deriving, using a right ventricular cardiac output (RVCO) algorithm, one or more cardiac output parameters from the ventricular hemodynamic data and/or the arterial hemodynamic data; deriving, using a venous oxygen saturation algorithm, one or more venous oxygen saturation parameters from the blood oxygen saturation data; deriving the GHI by using a predictive decision model based upon the one or more right ventricular pressure features, the one or more pulmonary artery features, the one or more cardiac output parameters, and the one or more venous oxygen saturation parameters; and displaying the GHI on a hemodynamic display.
16. 16. The method of claim 15, wherein the right ventricular pressure algorithm is applied to a right ventricular pressure waveform from the ventricular hemodynamic data to derive the one or more right ventricular pressure features, and wherein the one or more right ventricular pressure features include one or more of: a pulse rate, a maximum pressure rate of change with respect to time (“dP/dt”) during systolic rise, a minimum dP/dt during a relaxation period after end systole, a systole time, a systolic pressure, an end systolic pressure, an end diastolic pressure, a pulse pressure, and a mean pressure.
17. 17. The method of claim 15, wherein the PAP algorithm is applied to a pulmonary artery pressure waveform from the arterial hemodynamic data to derive the one or more pulmonary artery pressure features, and wherein the one or more pulmonary artery features include a mean pulmonary artery pressure.
18. 18. The method of claim 15, wherein the cardiac output parameters include a continuous cardiac output.
19. 19. The method of claim 15, wherein the venous oxygen saturation algorithm is used to derive a venous oxygen saturation value and a signal quality index from the arterial hemodynamic data and/or the ventricular hemodynamic data.
20. 20. The method of claim 15, further comprising: assigning a risk level to the global hypoperfusion index based on one or more predetermined thresholds, wherein the risk level is indicative of a patient condition based upon the global hypoperfusion index; and outputting a sensory alarm when the risk level indicates that a patient assessment is required, wherein the risk level indicating that patient assessment is recommended is output when a predicted venous oxygen saturation value is below 60.

Description

SYSTEMS AND METHODS TO PREDICT GLOBAL HYPOPERFUSION IN CRITICAL CARE PATIENTS CROSS-REFERENCE TO RELATED APPLICATION^ ) This application claims the benefit of U.S. Provisional Application No. 63/515,808, filed July 26, 2023, and entitled “SYSTEM AND METHOD TO PREDICT GLOBAL HYPOPERFUSION IN CRITICAL CARE PATIENTS,” and U.S. Provisional Application No. 63/623,048, filed January 19, 2024, and entitled “SYSTEMS AND METHODS TO PREDICT GLOBAL HYPOPERFUSION IN CRITICAL CARE PATIENTS,” the disclosures of which are hereby incorporated by reference in their entireties. BACKGROUND The present disclosure relates to hemodynamic monitoring and, in particular, to predicting global hypoperfusion of a patient. Global hypoperfusion describes the inadequate oxygen delivery to meet metabolic demands. A hypoperfusion event can be triggered by various physiological responses in the body, including problems with pulmonary system that result in reduction of oxygen supply, problems with the delivery of oxygen to the cells of the body (e.g., insufficient cardiac output (CO), low hemoglobin count, and/or bleeding events) or a sudden increase in oxygen demand. A global hypoperfusion event in a patient can result in serious harm and thus, a method of predicting when a global hypoperfusion event will occur is desirable. SUMMARY A system for determining a global hypoperfusion index (GHI) representative of a prediction of a future global hypoperfusion event within a patient. The system includes an arterial blood pressure sensor including a housing, a fluid input port connected via tubing to a fluid source, a catheter-side fluid port connected to a catheter inserted within an arterial system of a patient, a pressure transducer in communication with the fluid source through the fluid port, and an I/O cable in electrical communication with the pressure transducer. The system further includes a ventricular blood pressure sensor including a housing, a fluid input port connected via tubing to a fluid source, a catheterside fluid port connected to a catheter inserted within a ventricular system of a patient, a pressure transducer in communication with the fluid source through the fluid port, and an I/O cable in electrical communication with the pressure transducer. The system further includes an oximetry module including an optical transmitter, an optical receiver, and an I/O cable in electrical communication with the optical transmitter and the optical receiver. The system also includes an integrated hardware unit including a system processor, a system memory, a display including a user interface, and an analog-to-digital (ADC) converter. The system memory includes instructions that, when executed by the system processor, cause the system to receive arterial hemodynamic data from the arterial blood pressure sensor, receive ventricular hemodynamic data from the ventricular blood pressure sensor, and receive a blood oxygen saturation data from the oximetry module. One or more right ventricular pressure features are derived, using a first algorithm, from the ventricular hemodynamic data. One or more pulmonary artery pressure features are derived, using a second algorithm, from the arterial hemodynamic data. One or more cardiac output parameters are derived, using a third algorithm, from the ventricular hemodynamic data and/or the arterial hemodynamic data. One or more venous oxygen saturation parameters are derived, using a fourth algorithm, from the blood oxygen saturation data. The GHI is derived by using a predictive decision model based upon the one or more right ventricular pressure features, the one or more pulmonary artery features, the one or more cardiac output parameters, and the one or more venous oxygen saturation parameters. The GHI is displayed on the display. A method for determining a global hypoperfusion index (GHI) representative of a prediction of a future global hypoperfusion event within a patient includes receiving a plurality of hemodynamic data from arterial and ventricular blood pressure sensors, receiving a blood oxygen saturation data from an oximetry module, deriving, using a first algorithm, one or more right ventricular pressure features from the plurality of right ventricular hemodynamic data, deriving, using a second algorithm, one or more pulmonary artery pressure features from the plurality of pulmonary arterial hemodynamic data, deriving, using a third algorithm, one or more cardiac output parameters from the plurality of hemodynamic data, deriving, using a fourth algorithm, one or more venous oxygen saturation parameters from the blood oxygen saturation data, deriving the GHI by using a predictive decision model based upon the one or more right ventricular pressure features, the one or more pulmonary artery features, the one or more cardiac output parameters, and the one or more venous oxygen saturation parameters, and displaying the GHI on a hemodynamic display. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is