Systems and Methods for Transitioning Patient Care from Signal Based Monitoring to Risk Based Monitoring

• US 20150347704A1
• Filed: 06/01/2015
• Published: 12/03/2015
• Est. Priority Date: 12/16/2011
• Status: Active Grant

• Current Assignee: Etiometry Inc.
• Original Assignee: Etiometry Inc.
• Inventors: Baronov, Dimitar V., Butler, Evan J., Lock, Jesse M., McManus, Michael F.
• Granted Patent: US 10,062,456 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• 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 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/14542 for measuring blood gases A61B5/14551 takes precedence
• CPC Class Codes: A61B 5/14546 for measuring analytes not otherwise provided for, e.g. ions, cytochromes
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

A risk-based patient monitoring system for critical care patients combines data from multiple sources to assess the current and the future risks to the patient, thereby enabling providers to review a current patient risk profile and to continuously track a clinical trajectory. A physiology observer module in the system utilizes multiple measurements to estimate Probability Density Functions (PDF) of a number of Internal State Variables (ISVs) that describe a components of the physiology relevant to the patient treatment and condition. A clinical trajectory interpreter module in the system utilizes the estimated PDFs of ISVs to identify under which probable patient states the patient can be currently categorized and assign a probability value that the patient will be in each of the identified states. The combination of patient states and their probabilities is defined as the clinical risk to the patient.

Claims

1. A method of risk-based monitoring of a critical care patient, the method comprising:

providing a plurality of sensors including at least a heart rate sensor and an SpO2 sensor, the plurality of sensors being configured to be physically attachable with the critical care patient;

attaching the plurality of sensors to the patient;

substantially continuously acquiring, by a computer, physiological data from the plurality of sensors connected with the patient;

substantially continuously estimating a clinical trajectory for the patient, the patient'"'"'s clinical trajectory being described by probabilities of possible patient states using data acquired at a subsequent time step t_k+1 from at least the heart rate sensor and the SpO2 sensor attached to the patient, and posterior predicted probability density functions from a previous time step t_k, by;

generating, by the computer, predicted probability density functions of internal state variables for the time step t_k+1, each of the internal state variables describing a parameter physiologically relevant to at least one of a treatment and a condition of said patient at time step t_k+1, wherein the predicated probability density functions are calculated using posterior estimated probability density functions for each of the internal state variables from a preceding time step t_k;

generating, with the computer and using Bayes theorem, posterior predicted probability density functions for the plurality of the internal state variables for the time step t_k+1 at least by computing the conditional probability density functions of the data acquired at a time step t_k+1 given the internal state variables and the predicated probability density functions of internal state variables; and

identifying, with the computer, from the generated posterior predicted probability density functions of the internal state variables at time step t_k+1, into which of a first plurality of possible patient states the patient is currently categorizable;

generating a probability value associated with each identified possible patient state; and

substantially continuously displaying a clinical trajectory of the patient on a graphical user interface, the user interface being configured to display the probabilities of possible patient states as function of a plurality of time steps.

2. The method of risk-based monitoring of a critical care patient of claim 1, wherein the probability value associated with the identified possible patient states is between 0% and 100%.

3. The method of risk-based monitoring of a critical care patient of claim 1, further comprising:

assigning a hazard level associated with each of the identified possible patient states, and

presenting the probability values and hazard levels associated with the respective identified possible patient states.

4. The method of risk-based monitoring of a critical care patient of claim 1, wherein generating, with the computer, predicted probability density functions for the plurality of the internal state variables comprises:

comparing a newly received measurement associated with the internal state variable with a predetermined predicted likelihood of probable measurements given previously received measurements; and

not incorporating the newly received measurement into the predicted probability density function for the associated internal state variable, if the newly received measurement is not within the predetermined predicted likelihood of probable measurements for the associated internal state variable.

5. The method of risk-based monitoring of a critical care patient of claim 1, wherein identifying a first plurality of possible patient states and generating a probability value associated with each identified possible patient state comprises:

receiving, from a source, external computational data in the form of a probability value associated with a new attribute describing a patient state not within the first plurality of possible patient states; and

identifying, by the computer, from the generated probability density functions of the internal state variables and the probability value associated with the new attribute, into which of a second plurality of possible patient states, the patient is currently categorizable; and

generating a probability value associated with each identified possible patient state.

6. The method of risk-based monitoring of a critical care patient of claim 1, wherein generating, by the computer, predicted probability density functions for the plurality of the internal state variables for the time step t_k+1 comprises:

generating predicted probability density functions for a first plurality of the internal state variables at a time step t_k;

receiving, from a source, external computational data associated with a particular one of the plurality of the internal state variables; and

generating probability density functions for the plurality of the internal state variables at another time step t_k+1 from;

the probability density functions generated at a time step t_k and from;

received measurements associated with respective ones of the internal state variables and;

the external computational data associated with the particular one of the plurality of the internal state variables.

7. The method of risk-based monitoring of a critical care patient of claim 1, wherein the predicated probability density functions are calculated using posterior predicted probability density functions for each of the internal state variables from the preceding time step t_k, using the formula:

P(ISVs(t_k+1)|M(t_k))=∫

<sub>ISVsε

ISV</sub>P(ISVs(t_k+1)|ISVs(t_k))P(ISVs(t_k)|M(t_k))dISVs.

8. A risk based monitoring system for monitoring a critical care patient, comprising:

a processor;

a memory coupled to the processor;

a display operably coupled to the processor;

a plurality of sensors including at least a heart rate sensor and an SpO2 sensor, the plurality of sensors being configured to be physically attachable with the critical care patient;

a data reception module, operably coupled to the plurality of sensors for acquiring data associated with a plurality of the internal state variables at a time step t_k+1 each describing a parameter physiologically relevant to at least one of a treatment and a condition of a patient;

a physiology observer module, in communication with the data reception module, the physiology observer module configured to

generate predicted probability density functions of internal state variable for the time step t_k+1, each of the internal state variables describing a parameter physiologically relevant to at least one of a treatment and a condition of a patient at the time step t_k+1, and to

generate, using Bayes theorem, posterior predicted probability density functions for each of the internal state variable for the time step t_k+1 at least by computing conditional probability density functions of the data acquired by the data reception module given the internal state variables and the predicted probability density functions of internal state variables;

a clinical trajectory interpreter module, in communication with the physiology observer module, configured to identify, from the generated posterior density functions of the internal state variables at time step t_k+1, which of a first plurality of possible patient states the patient is currently categorizable and configured to generate a probability value associated with each identified possible patient state; and

a user interaction module configured to;

display, on the display device, a plurality of graphical indicators, each of the plurality of graphical indicators corresponding to one of the plurality of possible patient states, each of the plurality of graphical indicators graphically identifying the probability that the patient is in a corresponding patient state at a given point in a range of time, the plurality of graphical indicators configured to indicate a hazard level; and

to

display, on the display device, a timeline controller configured to allow a user to dynamically select a plurality of points in time over the range of time, the graphical indicators changing dynamically in response to a specification by the user of one of the plurality of points in time to display the evolution of the plurality of possible patient states over the range of time.

9. The system of claim 8, wherein the physiology observer module further comprises:

a dynamic model and an observation model stored in the memory.

10. The system of claim 8, wherein the physiology observer module compares a newly received measurement associated with an the internal state variable with a predetermined predicted likelihood of probable measurements given previously received measurements, and does not incorporate the newly received measurement into the predicted probability density function for the associated internal state variable, if the newly received measurement is not within the predetermined predicted likelihood of probable measurements for the associated internal state variable.

11. The system of claim 8, wherein the clinical trajectory interpreter module receives external computational data in the form of a probability value associated with a new attribute describing a patient state not within the first plurality of possible patient states, and identifies, from the generated predicted probability density functions of the internal state variables and the probability value associated with the new attribute, into which of a second plurality of possible patient states, the patient is currently categorizable and for generating a probability value associated with each identified possible patient state.

12. The system of claim 8, wherein:

the physiology observer module generates predicted probability density functions for the first plurality of the internal state variables at a time step t_k; and

the physiology observer module generates probability density functions for the plurality of the internal state variables at another time step t_k+1 from

the probability density functions generated at a time step t_k and from

received measurements associated with respective ones of the internal state variables and from

external computational data associated with the respective ones of the plurality of the internal state variables.

13. The system of claim 8, wherein the predicted probability density functions for the time step t_k+1 are generated using posterior predicted probability density functions for each of the internal state variables from a preceding time step, using the formula:

P(ISVs(t_k+1)|M(t_k))=∫

<sub>ISVsε

ISV</sub>P(ISVs(t_k+1)|ISVs(t_k))P(ISVs(t_k)|M(t_k))dISVs.

14. A computer-implemented method for risk based monitoring of a patient, comprising:

providing a computer having a display device and a computer accessible memory;

providing a plurality of physiological sensors including at least a heart rate sensor and an SpO2 sensor, the plurality of sensors physically attached to the critical care patient;

acquiring, with the computer from at least the physiological sensors coupled to the patient, data associated with a plurality of internal state variables each describing a parameter physiologically relevant to one of a treatment and a condition of the patient, wherein some of the data associated with the plurality of the internal state variables is intermittent or aperiodic;

storing, in the computer accessible memory, the acquired data associated with the plurality of the internal state variables;

generating, by the computer, predicted probability density functions for the plurality of the internal state variables at time step t_k;

generating, by the computer, predicted probability density functions for the plurality of the internal state variables at previous time step t<sub>k−

1</sub>, by evolving backwards from the predicted probability density functions at time step t_k to the time step t<sub>k−

1</sub>;

generating, by the computer using Bayes theorem, posterior probability density functions for the internal state variables at the previous time step t<sub>k−

1</sub>;

identifying, by the computer, from the generated predicted probability density functions of the internal state variables for the time step t_k, into which of a first plurality of possible patient states, the patient could have previously been categorizable and generating a probability value associated with each identified possible prior patient state;

causing the display, on a display device, of a plurality of graphical indicators, each of the plurality of graphical indicators corresponding to one of the plurality of possible patient states, each of the plurality of graphical indicators graphically identifying the probability that the patient is in a corresponding patient state at a given point in a range of time, the plurality of graphical indicators configured to indicate a hazard level; and

causing the display, on the display device, of a timeline controller configured to allow a user to dynamically select a plurality of points in time over the range of time, the graphical indicators changing dynamically in response to a specification by the user of one of the plurality of points in time to display the evolution of the plurality of possible patient states over the range of time.

15. The computer-implemented method of claim 14, wherein the predicted probability density functions for the plurality of the internal state variables at previous time step t_k−

1 are generated using the formula;

P(ISVs(t<sub>k−

1</sub>)|M(t_k))=∫

<sub>ISVsε

ISV</sub>P(ISVs(t<sub>k−

1</sub>)|ISVs(t_k))P(ISVs(t_k)|M(t_k))dISVs.

16. The method of claim 14, wherein a second plurality of internal state variables each describing a parameter physiologically relevant to one of a treatment and a condition of a patient associated with a time step t_k have no acquired data associated therewith and wherein the step of generating, with a computer, predicted probability density functions for the plurality of the internal state variables for previous time step t_k−

1 further comprises;

generating predicted probability density functions for the second plurality of the internal state variables at a time step t<sub>k−

1</sub>, at least by computing the conditional probability of internal state variables given initial estimates of values for the internal state variables; and

generating probability density functions for the second plurality of the internal state variables at another time time step t_k from the probability density functions generated at a time step t<sub>k−

1</sub> and from probability density functions associated with other internal state variables at a time step t_k.