METHODS AND SYSTEMS FOR ASSESSING CLINICAL OUTCOMES

• US 20090318775A1
• Filed: 03/26/2009
• Published: 12/24/2009
• Est. Priority Date: 03/26/2008
• Status: Abandoned Application

• Current Assignee: Theranos Incorporated
• Original Assignee: Theranos Incorporated
• Inventors: Michelson, Seth, Kemp, Timothy Michael, Gibbons, Ian, Holmes, Elizabeth A.
• Application Number:
  US12/412,334
• Publication Number:
  US 20090318775A1
• Time in Patent Office: Days
• US Class Current: 600/301
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/30 Unsupervised data analysis
• CPC Class Codes: G16C 20/70 Machine learning, data mining or chemometrics
• CPC Class Codes: G16C 20/90 Programming languages; Computing architectures; Database systems; Data warehousing
• CPC Class Codes: G16H 40/20 for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

Described herein are methods and systems useful for characterizing clinical outcomes of a subject. Provided herein includes computer-assessed methods, medical information systems, and computer-readable instructions that can aid an end-user in diagnosis, prognosis, and treatment of a clinical outcome.

Claims

1. A method for characterizing the probability of a clinical outcome of a subject, comprising:

a. constructing a probability space defined by a set of discrete clinical outcomes, each of which is characterized by a statistical distribution of at least one biological marker;

b. obtaining subject data corresponding to the at least one biological marker; and

c. calculating the position of said subject data in said probability space, thereby characterizing the probability of the clinical outcome of said subject.

4. The method of claim 1 or 3, wherein at least steps b and c are repeated at various time points to yield a trajectory within a probability space, wherein said trajectory is indicative of the likelihood of progression to the clinical outcome.

5. The method of claim 1 or 3 further comprising notifying a medical personnel or the subject of a need for taking a medical action upon assessing or characterizing the position of said subject data in said probability space.

6. The method of claim 5 wherein the medical action involves at least one action selected from the group consisting of altering a dosage of an existing therapeutic agent administered to said subject, administering a different a therapeutic agent, and administering a different combination of therapeutic agents.

7. The method of claim 5, wherein the notification is electronically transmitted.

8. The method of claim 5, wherein the notification is wirelessly transmitted.

9. The method of claim 5 further comprising, upon selection of the at least one action, performing an outcome analysis for assessing a result of said selected action, and automatically updating the probability of a discrete clinical outcome of said subject.

10. The method of claim 4, wherein the various time points cover a period of less than about 24 hours.

11. The method of claim 1 or 2 wherein the clinical outcome is selected from the group consisting of complete response (CR), partial response (PR), stable disease (SR), non-response(NR), adverse drug effect (ADR), and drug toxicity.

2. A medical information system for subject data analysis comprising:

a. an input device for receiving subject data and in communication with a processor;

b. a storage unit in communication with the processor having a database for;

(i) storing data corresponding to a probability space defined by a set of discrete clinical outcomes, each of which is characterized by statistical distribution of at least one biological marker; and

(ii) storing subject data corresponding to the at least one biological marker;

c. a processor that calculates the position of said subject data in said probability space as a way of assessing the probability of a discrete clinical outcome of said subject; and

d. an output device that transmits information relating to the discrete clinical outcome of c) to an end user.

12. The medical information system of claim 2, wherein the end user is a medical personnel or the subject.

13. The medical information system of claim 2, wherein the end user is from a pharmaceutical company.

14. The medical information system of claim 2, wherein said output device transmits selected portions of the subject data and the probability space in response to instructions from the end user.

15. The medical information system of claim 2, wherein the storage unit stores historical reference data of a plurality of subjects in relationship to the at least one biological marker.

16. The medical information system of claim 2, wherein the data stored in the storage unit are selected from the categories consisting of pathology, anatomy, treatment option, treatment outcome, pharmacological parameter, pharmacokinetics parameter, psychological parameter, and genomic information.

17. The medical information system of claim 2, wherein the database is a public database.

18. The medical information system of claim 2, wherein the database is an internal database.

19. The medical information system of claim 2 wherein the information transmitted by the output device is encrypted.

20. The medical information system of claim 2 comprises a network.

21. The medical information system of claim 2, wherein the input device and/or the output device comprises a user interface that can remotely access the network.

22. The medical information system of claim 2, wherein the processor calculates the position of said subject data in said probability space as a way of assessing the probability of a discrete clinical outcome of said subject.

23. The medical information system of claim 2, wherein the input device comprises a touch screen.

24. The medical information system of claim 2, wherein the input device comprises a data entry portal or a keyboard.

25. The medical information system of claim 2, wherein the subject data are textual or numeric.

26. The medical information system of claim 25, wherein the textual or numeric information is solicited from the end user.

27. The medical information system of claim 2, wherein the subject data represent measurements of the at least one biological marker present in a bodily fluid.

28. The medical information system of claim 2, wherein the subject data represent measurements of the at least one biological marker present in blood.

29. The medical information system of claim 2, wherein the subject data represent measurements of the at least one biological marker present in blood.

30. The medical information system of claim 29, wherein the measurements are obtained by a point-of-care device that is operated by the subject.

31. The medical information system of claim 30, wherein the measurements are taken at various time points to yield a trajectory within the probability space, wherein said trajectory represents a time series of the assessed clinical outcome.

32. The medical information system of claim 31, wherein the information transmitted by the output device represents an assessment of the clinical outcome of said subject at a single time point.

33. The medical information system of claim 31, wherein the information transmitted by the output device represents a time series of the assessed clinical outcome.

34. The medical information system of claim 2, wherein the output device comprises an automatic alert system.

35. The medical information system of claim 34, wherein the automatic alert system is programmable by the end user.

36. The medical information system of claim 34, wherein the automatic alert system is programmable based on a predefined protocol for a clinical trial.

37. The medical information system of claim 2, wherein the subject data represent measurements of the at least one biological marker present in blood.

38. The medical information system of claim 2, wherein the end user is a health care provider.

39. The medical information system of claim 37, wherein the health care provider is a Health Maintenance Organization (HMO).

3. A method of characterizing a clinical outcome of a subject comprising:

a. constructing a probability space within a server, wherein the probability space is defined by a set of discrete clinical outcomes, each of which is characterized by the statistical distribution of at the least one biological marker;

b. entering data of a subject into the server, said data corresponding to the at least one biological marker; and

c. calculating the position of said subject data in said probability space thereby characterizing the clinical outcome of the subject.

40. A computer readable medium comprising computer readable instructions, which when executed cause a processor to:

a. provide a probability space defined by a set of discrete clinical outcomes, each of which is characterized by a statistical distribution of at least one biological marker;

b. obtain subject data corresponding to the at least one biological marker; and

c. calculate the position of said subject data in said probability space to assess the probability of a clinical outcome of said subject.

41. The computer readable medium of claim 40, wherein the instructions operate in a software runtime environment.

42. The computer readable medium of claim 40, wherein the instructions when executed further causes a processor to provide a user defined alert condition based on an assessment of trajectory parameters of the subject data in the probability space, wherein said trajectory parameters are at least one of speed, acceleration, direction, and position.

43. A method of predicting the occurrence of a medical condition that requires medical intervention, the method comprising:

a. measuring concentrations of a first set of biomarkers present in a subject and measuring one or more physiological indicators of said subject at a given frequency, wherein the first set of biomarkers are suspected to be predictive of the medical condition;

b. based on the concentrations measure in (a), generating from the first set a subset of biomarkers that are more correlative with the occurrence of the medical condition and/or a new frequency of measurement of the biomarkers; and

c. measuring concentrations of the subset of (b) and/or following the new frequency of measurement of one or more biomarkers, thereby predicting the occurrence of the medical condition.

44. The method of claim 43 further comprising analyzing data reflecting the concentrations and/or the physiological indicators with multivariate statistical software.

45. The method of claim 43, wherein the biological markers are present in a biological sample of said subject.

46. The method of claim 45, wherein the biological sample is diluted by an appropriate fold.

47. A method of monitoring sepsis development of a subject comprising:

measuring at least two parameters selected from the group of (1) body temperature of said subject, (2) protein C concentration of said subject, (3) interleukin 6 (IL-6) concentration of said subject, multiple times to yield a trend of temperature, protein C trend, and/or IL-6; and

wherein an increase beyond normal body temperature, a decrease in protein C concentration and/or an increase in IL-6 concentration is indicative of the development of sepsis in said subject.

48. The method of claim 47, wherein a decrease in protein C followed by an increase of IL-6 is indicative of the development of sepsis in said subject.

49. The method of claim 47, wherein a decrease in protein C followed by an increase of IL-6 and an increase beyond normal body temperature is indicative of the development of sepsis in said subject.

50. The method of claim 47, wherein at least about 10-fold increase in IL-6 concentration in said subject is indicative of the development of sepsis in said subject.

51. The method of claim 47, wherein at least about 100-fold increase in IL-6 concentration in said subject is indicative of the development of sepsis in said subject.

52. The method of claim 47 further comprising the step of increasing frequency of measuring IL-6 concentration upon an increase beyond normal body temperature and/or a decrease in protein C concentration.

53. The method of claim 47, wherein the frequency is increased to once a day, once every 12, 8, 6, or 4 hours.

54. A method for characterizing a medical condition of a subject, comprising:

a. obtaining a first set of subject data comprising at least one biological marker and at least one physiological parameter from the subject;

b. determining the probability of a medical condition of the subject using the first set of subject data obtained;

c. selecting a second set of subject data from the probability of the medical condition; and

d. obtaining the second set of subject data from the subject, thereby characterizing the medical condition of the subject.

55. A method for characterizing periodicity of a clinical condition of a subject, the method comprising:

a. identifying a set of biomarkers for a clinically relevant condition;

b. obtaining longitudinal subject data corresponding to at least one biomarker in said set to obtain a trend of the subject data;

c. analyzing said trend to identify periodic changes in the at least one biomarker;

d. measuring values of peak measurements of the periodic changes of the trend; and

e. characterizing the values of the peaks thereby characterizing the periodicity of the clinically relevant condition.

56. The method of claim 55, wherein the analyzing step comprises developing an ARIMA model to determine a differencing lag in the underlying model.

57. The method of claim 56, wherein the differencing lag is used to de-trend the trend and establish a stationary trend.

58. The method of claim 55, wherein the analyzing step comprises calculating an autocorrelation function.

59. The method of claim 58, wherein the measuring step comprises identifying the statistically significant peaks in the autocorrelation function.

60. The method of claim 55, wherein the analyzing step comprises calculating spectral density.

61. The method of claim 60, wherein the calculating spectral density is performed using a Fast Fourier Transform.

62. The method of claim 55, wherein the measuring step comprises identifying the power spectrum of the maximum spectral density frequency.

63. A method for monitoring subject response to therapy comprising:

a. obtaining longitudinal subject data corresponding to at least one biomarker in a set of biomarkers for a clinically relevant condition to obtain a trend of the subject data, wherein the subject data is obtained from a subject receiving a therapy;

b. monitoring periodicity of the trend; and

c. corresponding the periodicity to a response to the therapy received by the subject.

64. The method of claim 63, wherein the therapy is a periodic dosing regimen.

65. The method of claim 63, wherein the response to the therapy is characterized by a time-dependent behavior of peak levels of the trend.

66. The method of claim 65, wherein the time-dependent behavior is substantially constant.

67. The method of claim 65, wherein the time-dependent behavior is changing linearly.

68. The method of claim 65, wherein the time-dependent behavior is changing exponentially.

69. A method for characterizing the emergence of clinically relevant subpopulations of patients exposed to a therapeutic agent, the method comprising:

a. identifying a set of biomarkers in a blood sample that act as surrogate markers for the therapeutic agent;

b. measuring the set of biomarkers longitudinally from a group of patients exposed to the therapeutic agent;

c. identifying distinct clusters in a multivariate clustering space of the measured values of the set of biomarkers from the group of patients;

d. determining the rate of separation of the distinct clusters and measuring the distance between the distinct clusters in a statistical manner;

e. obtaining patient information from the group of patients to classify the patients in clinically relevant subpopulations; and

f. comparing the distinct clusters to the clinically relevant subpopulations to characterize sensitivity and specificity of the distinct clusters to predict the clinically relevant subpopulations.

70. The method of claim 69 further comprising identifying a second set of biomarkers configured to improve the characterization of the emergence of distinct clusters to predict the clinically relevant subpopulations.

71. The method of claim 69, wherein the group of patients exposed to the therapeutic agent are participants in a clinical trial.

72. The method of claim 71, wherein the clinical trial is a dose ranging trial.

73. The method of claim 71, wherein the clinical trial is a part of an adaptive clinical trial.

74. The method of claim 73, wherein the adaptive clinical trial is designed to characterize an optimal dosing regimen.

75. The method of claim 73, wherein the adaptive clinical trial is designed to characterize an optimal responder population.

76. The method of claim 71, wherein the measuring the distance step comprises measuring the Mahalanobis distance between the distinct cluster centroids.

77. The method of claim 71, wherein the measuring the distance step comprises measuring the nearest-neighbors distance between the distinct clusters.

78. The method of claim 71, wherein the measuring the distance step comprises measuring a Euclidean distance measure between the distinct clusters.

79. The method of claim 71, wherein the measuring the distance step measuring a Manhattan distance measure between the distinct cluster.