Systems and methods for transitioning patient care from signal based monitoring to risk based monitoring

• US 10,062,456 B2
• Filed: 06/01/2015
• Issued: 08/28/2018
• 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.
• Primary Examiner(s): Gort, Elaine
• Assistant Examiner(s): Go, John P
• Application Number:
  US14/727,696
• Publication Number:
  US 20150347704A1
• Time in Patent Office: 1,184 Days
• Field of Search: 705 2, 705 3, 705 4
• 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 risk:

based monitoring system for transforming measured data, including heart rate and blood oxygen, of a critical care patient into hidden internal state data that is not directly measurable with sensors, the hidden internal state being monitored by the system, the system comprising;

a processor;

a memory coupled to the processor;

a display operably coupled to the processor;

a data reception module, the data reception module having a set of inputs for a plurality of sensors including at least a heart rate sensor and an SpO2 sensor, the plurality of sensors being couplable to the critical care patient, wherein the plurality of sensors provide, to the data reception module, data associated with a corresponding plurality of internal state variables m_s, S=1, . . . , n, over a series of time steps t_k, K=0, 1, . . . Z each internal state variable m_s characterizing a parameter physiologically relevant to at least one of a treatment and a condition of the patient;

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

update, via a first and second computer processes, the data provided by the sensors to the data reception module, wherein;

the first computer process generates a conditional likelihood kernel for the internal state variables m_S at time t_k, the conditional likelihood kernel comprising a set of probability density functions, each such probability density function being for a distinct internal state variable m_S at time t_k; and

the second computer process generates,

using Bayes theorem, posterior predicted conditional probability density functions for each of the internal state variables m_s for the time step t_k given the conditional likelihood kernel for the internal state variables at time t_k and a predicted conditional probability density function of each of the internal state variables for time t_k; and

a clinical trajectory interpreter module, in communication with the physiology observer module, configured to determine, based on the generated posterior predicted conditional probability density functions of the internal state variables m_s at time step t_k, a set of possible states of a hidden internal state variable to generate a probability value representing the likelihood that the patient'"'"'s oxygen delivery, which cannot be directly measured, is inadequate; 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 states of the set of possible states of the hidden internal state variable, each of the plurality of graphical indicators graphically identifying the probability that the hidden internal state variable is in a corresponding state at a given point in a range of time, the plurality of graphical indicators configured to indicate a hazard level.

2. The system of claim 1, wherein the user interaction module is further configured to:

display, on the display device, a clinical trajectory of the patient based on the conditional probability density function of the hidden internal state variable over at least some of the series of time steps.

3. The system of claim 1, wherein the physiology observer module compares a newly received measurement associated with an internal state variable m_s at time t_k with a predetermined predicted likelihood of probable measurements given previously received measurements, and does not generate a conditional probability density function for the associated internal state variable m_s, if the newly received measurement is not within the predetermined predicted likelihood of probable measurements for the associated internal state variable m_s.

4. The system of claim 1, further comprising a timeline controller configured to allow a user to dynamically select a plurality of points in time over the series of time steps t_k, 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 hidden internal state variable.

5. The system of claim 1, wherein:

the physiology observer module is further configured to generate, at time step t_k, a predicted conditional probability density function for each of the internal state variables m_S for time t_k+1 based on the posterior predicted probability density functions for each of the internal state variable m_S for the time step t_k.

6. The system of claim 5, wherein the conditional probability density functions for the time step t_k+1 are generated using the posterior conditional probability density functions for each of the internal state variables m_s from a 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.

7. A method of transforming measured data, including heart rate and blood oxygen, of a critical care patient into hidden internal state data which is not directly measurable with sensors, 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, from the plurality of sensors connected with the patient, physiological data associated with a corresponding plurality of internal state variables m_s, S=1, . . . n, over a series of time steps t_k, K=0, 1, . . . Z;

generating, by the computer, a conditional likelihood kernel for the internal state variables m_s at time t_k, the conditional likelihood kernel comprising a set of probability density functions of the internal state variables m_s for the time step t_k, 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;

generating, with the computer and using Bayes theorem, posterior predicted conditional probability density functions for the plurality of the internal state variables m_s for the time step t_k given the conditional likelihood kernel for the internal state variables m_s at time t_k and predicted probability density functions of each of the internal state variables m_s for time step t_k; and

identifying, with the computer, from the generated posterior predicted conditional probability density functions of the internal state variables m_s at time step t_k, a set of possible states of a hidden internal state variable associated with oxygen delivery (DO₂), and generating a probability value representing the likelihood that the patient'"'"'s oxygen delivery (DO₂) is inadequate, which cannot be directly measured

generating a probability value associated with the hidden internal state; and

substantially continuously displaying a clinical trajectory of the patient on a graphical user interface, the user interface being configured to display the probability of the hidden internal state variable as function of a plurality of time steps.

8. The method of claim 7, wherein the probability value associated with the hidden internal state variable indicates risk that the patient'"'"'s oxygen delivery is inadequate and the patient may be in a state of shock.