SYSTEMS AND METHODS FOR TRANSITIONING PATIENT CARE FROM SIGNAL-BASED MONITORING TO RISK-BASED MONITORING

• US 20130231949A1
• Filed: 03/14/2013
• Published: 09/05/2013
• Est. Priority Date: 12/16/2011
• Status: Active Application

• Current Assignee: Etiometry Inc.
• Original Assignee: Etiometry Inc.
• Inventors: Baronov, Dimitar V., Butler, Evan J., Lock, Jesse M., McManus, Michael F.
• Application Number:
  US13/826,441
• Publication Number:
  US 20130231949A1
• Time in Patent Office: Days
• US Class Current: 705/2
• 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 computer-implemented method for risk-based monitoring of patients, comprising:

acquiring, with a computer, data associated with a plurality of the internal state variables each describing a parameter physiologically relevant to at least one of a treatment and a condition of a patient;

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

generating, with a computer, estimated probability density functions for the plurality of the internal state variables;

identifying, with a computer, from the generated probability density functions of the internal state variables, into which of a first plurality of possible patient states the patient is currently categorizable and;

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

2. The method of claim 1, wherein the probability value associated with the identified possible patient states is between 0% and 100%.

3. The method of claim 1, further comprising:

presenting, through a user interface, the probability values and their associated respective identified possible patient states.

4. The method 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.

5. The method of claim 1, wherein generating, with a computer, estimated probability density functions for the plurality of the internal state variables comprises:

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

6. The method of claim 5, wherein each of the received measurements of respective of the internal state variables are associated with a same time step.

7. The method of claim 5, wherein not all of the received measurements of respective of the internal state variables are associated with a same time step.

8. The method of claim 1, wherein generating, with a computer, estimated probability density functions for the plurality of the internal state variables comprises:

comparing a newly received measurement associated with an 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 estimated 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.

9. The method of claim 1, wherein identifying a first plurality of possible patient states and generating a probability value associated with each identified possible patient state comprise:

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, with a 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 states.

10. The method of claim 1, wherein generating, with a computer, estimated probability density functions for the plurality of the internal state variables comprises:

generating estimated probability density functions for the 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 of the internal state variables and the external computational data associated with the particular one of the plurality of the internal state variables.

28. The method of claim 1, wherein a second plurality of the internal state variables each describing a parameter physiologically relevant to one of a treatment and a condition of a patient have no acquired data associated therewith and wherein generating, with a computer, estimated probability density functions for the plurality of the internal state variables comprises:

generating estimated probability density functions for the second plurality of the internal state variables at a time step t_k; and

generating probability density functions for the second plurality of the internal state variables at time step t_k+1 from the probability density functions generated at a time step t_k and from probability density functions associated with other internal state variables at a time step t_k.

29. The method of claim 5, wherein generating probability density functions for the plurality of the internal state variables at another time step t_k+1 further comprises generating the probability density functions from received measurements associated with internal state variables.

11. A risk based monitoring system for monitoring patients, comprising:

a processor;

a memory coupled to the processor;

a data reception module, operably coupled to a plurality of sources of information relative to a patient, for acquiring data associated with a plurality of the internal state variables 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,

for generating probability density functions of the internal state variables;

a clinical trajectory interpreter module, in communication with the physiology observer module, for identifying into which of a first 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 11, further comprising:

a user interaction module, in communication with the clinical trajectory interpreter and memory, for presenting the probability values and their associated respective identified possible patient states.

13. The system of claim 11, wherein the physiology observer module further comprises:

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

14. The system of claim 13, wherein the physiology observer module further comprises:

an inference engine configured to interoperate with the dynamic model and the observation model is stored in memory.

15. The system of claim 13, wherein the physiology observer module has a predictive mode of operation in which the estimated probability density functions for the plurality of the internal state variables at a time step t_k, are provided to the dynamic model, to produce estimated probability density functions for the plurality of the internal state variables at another time step t_k+1.

16. The system of claim 14, wherein not all of the received measurements of respective of the internal state variables are associated with a same time step.

17. The system of claim 11, wherein the physiology observer module compares a newly received measurement of 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 estimated 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.

18. The system of claim 11, 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 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.

19. The system of claim 11, wherein the physiology observer module generates estimated probability density functions for the first plurality of the internal state variables at a time step t_k and 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 in at a time step t_k and from received measurements associated with respective of the internal state variables and from external computational data associated with the particular one of the plurality of the internal state variables.

20. A computer program product comprising a non-transitory computer-readable medium having executable instructions in the form of computer program code stored thereon comprising:

computer program code for acquiring data associated with a plurality of the internal state variables each describing a parameter physiologically relevant to at least one of a treatment and a condition of a patient;

computer program code for storing the acquired data associated with the plurality of the internal state variables;

computer program code for generating estimated probability density functions for the plurality of the internal state variables; and

computer program code for identifying from the generated probability density functions of the internal state variables, which of a plurality of possible patient states the patient is currently categorizable and generating a probability value associated with each identified patient state.

21. The computer program product of claim 20, wherein the probability value associated with the identified possible patient states is between 0% and 100%.

22. The computer program product of claim 20, further comprising:

computer program code for presenting the probability values and their associated respective identified patient states.

23. The computer program product of claim 20, further comprising:

computer program code for assigning a hazard level associated with each of identified possible patient states, and

computer program code for presenting the probability values and hazard levels associated the respective identified possible patient states.

24. The computer program product of claim 20, wherein computer program code for generating estimated probability density functions for the plurality of the internal state variables comprises:

computer program code for generating estimated probability density functions for the first plurality of the internal state variables at a time step t_k; and

computer program code for 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.

25. The computer program product of claim 20, wherein not all of the received measurements of respective of the internal state variables are associated with a same time step.

30. The method of claim 24, wherein computer program code for 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 further comprises generating the probability density functions for the plurality of the internal state variables at another time step t_k+1 from received measurements of respective of the internal state variables

26. A computer-implemented method for risk based monitoring of patients, comprising:

acquiring, with a computer, data associated with a plurality of the internal state variables each describing a parameter physiologically relevant to one of a treatment and a condition of a patient, not all of the data associated with the plurality of the internal state variables with at the same periodicity;

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

generating, with a computer, estimated probability density functions for the plurality of the internal state variables; and

identifying, with a computer, from the generated probability density functions of the internal state variables, into which of a first plurality of possible patient states, the patient could has previously been categorizable and generating a probability value associated with each identified possible prior patient state.

27. The method of claim 26, wherein generating, with a computer, estimated probability density functions for the plurality of the internal state variables comprises:

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

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

N</sub>, where N is an integer value greater than 1, by evolving backwards from the probability estimates at time step t_k to the time step t<sub>k−

N</sub> using a defined transition probability kernel.