Information processing method for disease stratification and assessment of disease progressing

• US 20040243362A1
• Filed: 06/14/2004
• Published: 12/02/2004
• Est. Priority Date: 06/01/2001
• Status: Abandoned Application

• Current Assignee: Prosanos Corp. (The Cigna Group)
• Original Assignee: Prosanos Corp. (The Cigna Group)
• Inventors: Liebman, Michael N.
• Application Number:
  US10/479,192
• Publication Number:
  US 20040243362A1
• Time in Patent Office: Days
• US Class Current: 703/2
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 20/20 Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• CPC Class Codes: G16B 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
• CPC Class Codes: G16B 25/10 Gene or protein expression profiling; Expression-ratio estimation or normalisation
• 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

Abstract

A digital computer system stratifies in a set of patients, based on a set of observations. The observations can include physical, biochemical, histological, genetic, and gene-expression data, among other types of information. Adjustments can be made to account for the possibility that observations of several patients may begin at different points in the progression of their respective disease processes. Once these adjustments are made, the data are subjected to a statistical cluster analysis. Each cluster of patients potentially represents a different disease stratum, with its own underlying cause, optimum therapy, and prognosis. Once the strata are defined and patients are assigned to them, adjustments to the data can be refined. The cluster analysis then can be repeated, and so an iterative process of stratification and staging takes place.

Claims

1. An information processing method for the stratification of a disease, and for the assessment of the disease'"'"'s progression, comprising the steps of:

(a) recording a time series of observations for a plurality of variables obtained from a plurality of patients who share the disease;

(b) entering and storing those observations as a data set in a computer, wherein the computer performs the subsequent steps as automated computations;

(c) selecting, for subsequent analysis, a subset of the data set, wherein the subset is based on patient demographic information or prior treatment history;

(d) stratifying the disease by clustering patients into strata, wherein the strata are based on the shapes of curves which represent the progression of said observations over time;

(e) using the strata created in step (d) or step (g) to align, truncate, or extend each time series so that data points compared in step (f) correspond to a similar disease stage for all patients;

(f) for each pair of patients, comparing the aligned time series to determine a measure of the mathematical distance between them; and

(g) refining the stratification of the disease by assigning the patients to clusters based on the mathematical distances determined in step (f), such that each cluster corresponds to a stratum of the disease.

2. The method of claim 1, wherein the variables include demographic data, biochemical data, pathologic data, histological data, genetic data, or gene-expression data, or any combination thereof.

3. The method of claim 1, further comprising the step of:

(h) reducing the number of variables used in subsequent analysis, by dimensionality-reduction to eliminate or combine variables.

4. The method of claim 3, wherein the method of dimensionality-reduction is principal-components analysis or factor analysis.

5. The method of claim 1, wherein the initial estimates of disease stage for step (d) are provided by a clinician or a published clinical disease-staging algorithm.

6. The method of claim 1, wherein the mathematical distance between time series is calculated point-wise using a Euclidean metric, city-block metric, or manually-prepared lookup table.

7. The method of claim 1, wherein the cluster assignments of step (f) are interactively modified by a human operator.

8. The method of claim 1, wherein the disease stratification and progression information is subsequently used to predict the course of an individual patient'"'"'s disease, by analogy to other patients at a similar stage in the same disease stratum.

9. The method of claim 1, wherein the disease stratification and progression information for a particular patient is submitted to a clinician to guide diagnosis and treatment for that patient.

10. The method of claim 1, further comprising the step of:

(h) combining the disease stratification information with genetic data, gene expression data, or biochemical data, to identify a biochemical target for drug development as therapy for a particular stratum or set of strata of the disease.

11. The method of claim 1, further comprising the step of:

(h) calculating, for each patient, an information representing the rate of change of some or all of the plurality of variables with respect to time and augmenting the data set with that information.

12. The method of claim 1, further comprising the step of:

(h) repeating steps (e) through (g), until the change in the progression and stratification estimates fall within a predetermined limit during each subsequent iteration.

13. The method of claim 1, further comprising the steps of:

(h) determining, statistically, which observed variable or variables provide the most information regarding said stratification, to develop a diagnostic marker kit for stratification of the disease under study.

14. The method of claim 1, further comprising the steps of:

(h) determining, based on the disease stratification information, lifestyle factors that are correlated with improved outcome for a particular stratum or set of strata of the disease; and

(i) recommending, based on said lifestyle factors, lifestyle changes to patients in a particular stratum or strata.

15. The method of claim 1, further comprising the additional steps of:

(h) recording a time series of observations of variables regarding an additional patient who share the disease;

(i) entering and storing those additional observations into the data set stored in the computer;

(j) estimating the stage of progression of the additional patient'"'"'s disease at the time of the first observation for the additional patient;

(k) using the estimates of step (j) to align, truncate, or extend the additional patient'"'"'s time series to reflect each subsequent new patient so that data points compared in step (l) correspond to a similar stage of disease for all patients;

(l) comparing, for each subsequent new patient, the aligned time series to determine a measure of the mathematical distance to the data of the patients within each cluster; and

(m) assigning the additional patient and the subsequent new patients to clusters based on the mathematical distances determined in step (l), thereby assigning them to a stratum of the disease.

16. The method of claim 15, further comprising the steps of:

(n) calculating, for the additional patient, an information representing the rate of change of some or all variables with respect to time, and augmenting the data set with that information.

17. The method of claim 15, further comprising the steps of:

(n) refining the estimate of the stage of progression of each patient'"'"'s disease using the stratification information obtained in step (m).