SYSTEMS AND METHODS FOR MEDICAL DATA PROCESSING AND ANALYSIS

• US 20150363560A1
• Filed: 01/30/2014
• Published: 12/17/2015
• Est. Priority Date: 01/30/2013
• Status: Active Grant

• Current Assignee: Attomarker Limited
• Original Assignee: Attomarker Limited
• Inventors: SHAW, Andrew Mark
• Granted Patent: US 10,446,264 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

We describe a method of predicting the response of a patient to a medical procedure, the method comprising: inputting acute phase response (APR) biomarker data defining a level of an acute phase response (APR) biomarker in said patient at a succession of biomarker measurement times following said medical procedure, said APR biomarker data defining a biomarker time course representing an evolution over time of said acute phase response; and processing said APR biomarker data to determine a derivative with respect to time of said time course from said APR biomarker data to provide APR time series data; determining a prediction of the response of said patient to said medical procedure from said APR time series data.

Claims

1. A method of predicting the response of a patient to a medical procedure, the method comprising:

inputting acute phase response (APR) biomarker data defining a level of an acute phase response (APR) biomarker in said patient at a succession of biomarker measurement times following said medical procedure, said APR biomarker data defining a biomarker time course representing an evolution over time of said acute phase response; and

processing said APR biomarker data to determine a derivative with respect to time of said time course from said APR biomarker data to provide APR time series data;

determining a prediction of the response of said patient to said medical procedure from said APR time series data.

2. A method as claimed in claim 1 wherein said determining of said prediction comprises identifying a maximum in said APR time series data, and predicting recovery of said patient, or increasing probability of a prediction of recovery of said patient, responsive to said identification of said maximum.

3. A method as claimed in claim 1 or 2 wherein said determining of said prediction comprises identifying a minimum in said APR time series data, and predicting complications in said patient, or increasing probability of a prediction of complications in said patient, responsive to said identification of said minimum.

4. A method as claimed in claim 1 wherein said processing of said APR biomarker data to determine said derivative with respect to time of said time course further comprises correcting for a clearance rate of said biomarker in said patient.

5. A method as claimed in claim 4 further comprising one or both of disregarding a data value and flagging an error, where said determined derivative has a value defining a rate of fall of said level of said APR biomarker greater than said clearance rate.

6. A method as claimed in claim 1 wherein said determining of said prediction comprises determining a value for an alert state variable responsive to said APR time series data;

and

determining said prediction from said alert state variable.

7. A method as claimed in claim 6 wherein said determining of said value for said alert state variable comprises identifying one or more of the following conditions:

(i) a falling level of said APR time series data after an initial delay time;

(ii) a point of inflection in said APR time series data;

(iii) a minimum in said APR time series data;

(iv) a rise in said APR time series data in response to a said biomarker measurement; and

(v) a fall in the APR time series data in response to a said biomarker measurement;

and, responsive to said identifying, adjusting said value for said alert state variable.

8. A method as claimed in claim 6 wherein said determining of said prediction comprises outputting early warning data defining at least one of a recovery prediction and a complication prediction, wherein said early warning data is determined responsive to said value of said alert state variable.

9. A method as claimed in claim 7 wherein said determining of said prediction comprises outputting early warning data defining at least one of a recovery prediction and a complication prediction, wherein said early warning data is determined responsive to said value of said alert state variable, and wherein said adjusting of said value for said alert state variable comprises adjusting to increase a recovery probability prediction for conditions (i) and (v) where one or more of conditions (i) and (v) is used, and adjusting to increase a complication prediction probability for conditions (ii), (iii) and (iv) where one or more conditions (ii), (iii) and (iv) is used.

10. A method as claimed in claim 6 wherein said determining of said prediction comprises determining, given said value of said alert state variable, a probability of one or more of a set of possible said responses.

11. A method as claimed in claim 10 wherein said determining of said prediction comprises determining the probability P(response|alert state variable) of a said response given said value of said alert state variable, comprises determining as a function of time:

P(alert statevariable|response)•

P(alert state variable)

where P(alert statevariable|response) is a probability of said alert state variable given said response, and P(alert state variable) is a prior probability of said alert state variable.

12. A method as recited in claim 11 wherein one or more terms of P(alert state variable response)•

P(alert state variable) is dependent upon a comorbidity of said patient.

13. A method as recited in claim 1 wherein said APR biomarker comprises CRP (C-Reactive Protein), and wherein said determining of said prediction comprises evaluating:

14. A method as recited in claim 1 wherein said medical procedure comprises surgery, the method further comprising performing a assay to determine said level of said APR biomarker.

19. A physical data carrier carrying computer program code to implement the method of claim 1.

15. A computer program for predicting the response of a patient to a medical procedure, wherein the program is configured to:

input APR biomarker measurement data defining a level of an APR biomarker;

update APR biomarker time series data for said APR biomarker responsive to said APR biomarker measurement data;

determine a time derivative of said updated APR biomarker time series data to determine updated APR data;

update stored time series of said APR data with said updated APR data;

identify one or more time dependent conditions in said stored time series of said APR data;

update a value of an alert state variable responsive to said identification of said one or more time dependent conditions; and

predict said response of said patient dependent on said value of said alert state variable.

16. A computer program as claimed in claim 15 wherein said time-dependent conditions comprise one or more of:

(i) a falling level of said APR time series data after an initial delay time;

(ii) a point of inflection in said APR time series data;

(iii) a minimum in said APR time series data;

(iv) a rise in said APR time series data in response to a biomarker measurement; and

(v) a fall in the APR time series data in response a biomarker measurement.

17. A computer program as claimed in claim 15 wherein said prediction of said response comprises determining, as a function of time:

P(alert statevariable|response)•

P(alert state variable)

where P(alert statevariable|response) is a probability of said alert state variable given said response, and P(alert state variable) is a prior probability of said alert state variable.

18. A computer program as calmed in claim 17 wherein said APR biomarker comprises CRP (C-Reactive Protein), and wherein said determining of said prediction comprises evaluating:

20. A medical procedure outcome prediction system, the system comprising:

working memory;

program memory;

a processor coupled to said working memory and to said program memory;

an input to receive APR biomarker data comprising data representing a level of an acute phase response (APR) biomarker in said patient at a succession of biomarker measurement times following said medical procedure, said APR biomarker data defining a biomarker time course representing an evolution over time of said acute phase response; and

wherein said program memory stores processor control code to;

process said APR biomarker data to determine a derivative with respect to time of said time course from said APR biomarker data to provide APR time series data; and

determine a prediction of the response of said patient to said medical procedure from said APR time series data.