Simulating Patient-Specific Outcomes

• US 20090150134A1
• Filed: 11/12/2008
• Published: 06/11/2009
• Est. Priority Date: 11/13/2007
• Status: Abandoned Application

• Current Assignee: Entelos, Inc. (SilkRoad Inc.)
• Original Assignee: Entelos, Inc. (SilkRoad Inc.)
• Inventors: Paterson, Thomas S., De Leon, Hector, Powell, Lyn, Kadambi, Ananth, Gadkar, Kapil
• Application Number:
  US12/269,852
• Publication Number:
  US 20090150134A1
• Time in Patent Office: Days
• US Class Current: 703/11
• CPC Class Codes: G16H 40/20 for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms

Abstract

The invention encompasses systems, methods, and apparatus for predicting clinical outcomes and monitoring an individual'"'"'s response to a therapeutic regimen. The invention further encompasses methods for predicting cardiovascular risk based a genetic marker status and methods for modifying a computer to reflect genetic data and for incorporating genetic markers into a virtual population.

Claims

1. A method for predicting cardiovascular risk based a genetic marker status, the method comprising:

(a) providing a genetic marker status of a subject;

(b) providing a computer model of cardiovascular risk configured to account for the genetic marker, wherein the computer model comprises;

(i) a representation of cholesterol metabolism,

(ii) a representation of atherogenesis, and

(iii) a representation of plaque stability,

wherein a positive genetic marker status is indicated by an alteration in at least one of cholesterol metabolism, atherogenesis and plaque stability; and

(c) simulating and reporting an outcome for the subject.

2. The method of claim 1, wherein the representation of plaque stability accounts for smooth muscle dynamics and a positive genetic marker status is indicated by an alteration in smooth muscle cell dynamics.

3. The method of claim 1, wherein the genetic marker is a single nucleotide polymorphism (SNP).

4. The method of claim 3, wherein the SNP is located at chromosomal locus 9p21.

5. The method of claim 4, wherein the SNP is rs10757278(G) and the genetic marker status is positive.

6. The method of claim 5, wherein the positive genetic marker status is indicated by increased smooth muscle cell apoptosis and decreased smooth muscle cell proliferation in a plaque.

7. The method of claim 1, wherein the computer model of cardiovascular risk further is configured to account for imaging data about a subject and wherein the imaging data is indicated in the model by an alteration of at least one of cholesterol metabolism, atherogenesis and plaque stability.

8. The method of claim 1, wherein the computer model of cardiovascular risk further is configured to account for a blood measurement about a subject and wherein the blood measurement is indicated in the model by an alteration of at least one of cholesterol metabolism, atherogenesis and plaque stability.

9. A computer-implemented method of predicting a clinical outcome for a subject comprising:

(a) providing a virtual population comprising a plurality of virtual patients;

(b) receiving input data about a subject;

(c) selecting one or more virtual patients from the virtual population based on a similarity between each of the selected virtual patients and the input data;

(d) applying one or more virtual protocols to the one or more selected virtual patients to generate a set of outputs projecting a clinical outcome for the subject, wherein a set of outputs is generated for each selected virtual patient; and

(e) reporting the set of outputs to a user.

10. The method of claim 9, wherein each virtual patient of the virtual population has an associated prevalence.

11. The method of claim 10, wherein applying one or more virtual protocols to the one or more selected virtual patients comprises calculating a likelihood of each clinical outcome based upon the prevalence of the one or more virtual patients.

12. The method of claim 11, wherein the set of outputs comprises the likelihood of each clinical outcome.

13. The method of claim 9, wherein the virtual population is a prevalence-weighted virtual population, wherein each virtual patient of the virtual population has an associated prevalence weight.

14. The method of claim 9, wherein the virtual protocol is selected from the group consisting of a therapeutic regimen, passage of time, exercise, weight gain, diet, a lifestyle choice and a combination of two or more of the same.

15. The method of claim 9, wherein the virtual population accounts for a genetic marker.

16. The method of claim 10, wherein an effect of the genetic marker is represented as one or more axes of variation within the virtual population, wherein each virtual patient of the virtual patient population has an associated prevalence.

17. A method for modifying a computer model of a biological system to reflect genomic information, the method comprising:

(a) providing a computer model of a biological system in a computer-readable storage medium;

(b) providing a genetic marker having a known association with a clinical phenotype, wherein the genetic marker has a known locus on a chromosome;

(c) identifying one or more genes of known biological function that have linkage disequilibrium with the locus of the genetic marker;

(d) modifying the computer model to reflect the function of the one or more identified genes; and

(e) storing the modified computer model in a computer-readable storage medium.

18. The method of claim 17, wherein the computer model is modified to reflect the function of the one or more identified genes.

19. The method of claim 17, wherein the computer model is modified to reflect absence of the function of the one or more identified genes.

20. The method of claim 17, further comprising the step of

(f) executing the modified computer model to generate a simulated outcome;

and

(g) comparing the simulated outcome with the known association between the genetic marker and clinical phenotype to confirm the validity of the modified computer model.

21. The method of claim 20, wherein comparing the simulated outcome with the known associate between the genetic marker and clinical phenotype comprises comparing a virtual population with a clinical population.

22. A computer model prepared in accordance with the method of claim 17.

23. A method of incorporating a genetic marker into a virtual population, said method comprising:

(a) providing an original virtual population having a set of population constraints;

(b) defining the effect of the genetic marker as one or more new axes of variation to be included in generating a new virtual population;

(c) generating virtual patients based on (i) the population constraints of the original virtual population and (ii) the one or more new axes of variation;

(d) assigning prevalence weights to the virtual population, incorporating population statistics for the genetic marker as a constraint in the prevalence weighting process; and

(e) generating an output comprising the virtual population and associated prevalence weights.

24. The method of claim 23, wherein the virtual population is provided as data stored on a computer readable medium.

25. The method of claim 23, wherein each allele for the genetic marker corresponds to a different quantitative position on the new axis of variation.

26. The method of claim 23, wherein the effect of the genetic marker is defined as a single new axis of variation.

27. The method of claim 23, wherein the effect of the genetic marker is defined as more than one new axis of variation.

28. The method of claim 23, wherein defining the effect of the genetic marker as one or more new axes of variation comprises:

(i) identifying the locus of the genetic marker on a chromosome;

(ii) identifying one or more genes of known biological function that have linkage disequilibrium with the locus of the genetic marker; and

(iii) defining the one or more new axes of variation based upon the known biological function of the one or more genes.

29. The method of claim 28, wherein the one or more new axes of variation reflect the function of the one or more identified genes.

30. The method of claim 28, wherein the one or more new axes of variation reflect absence of the function of the one or more identified genes.

31. The method of claim 28, wherein the one or more new axes of variation reflect a downstream effect of the function of the one or more identified genes.

32. A virtual population prepared in accordance with the method of claim 23.