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Method for improving disease diagnosis using measured analytes

A disease and a technology for diagnosing diseases, applied in the field of diagnostic testing, can solve problems such as non-biological relevance, non-correlation, difficulty in algorithm training, etc.

Pending Publication Date: 2019-09-27
OTRACES INC
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the problem with all of these approaches is that many of the proteins selected do not have a strong correlation with health-to-disease progression (and many have no known biological relevance to disease states, e.g., as is often the case with mass spectrometry. case)
Furthermore, mass spectrometry suffers from severe oversampling problems due to the fact that whole serum samples are interrogated for protein levels by a spectrophotometer and thus make training of correlation algorithms difficult

Method used

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  • Method for improving disease diagnosis using measured analytes
  • Method for improving disease diagnosis using measured analytes
  • Method for improving disease diagnosis using measured analytes

Examples

Experimental program
Comparison scheme
Effect test

example 1

[0133] Example 1 : Clinical Study - Evaluation of Blood Tests for Breast Cancer

[0134] OTraces BC Sera Dx Test Kit and OTraces CDx Immunochemistry Instrumentation System ( www.otraces.com ) properties to assess the risk of the presence of breast cancer. The test kit measures the concentrations of five very low-level cytokines and tissue markers and uses the training set model developed as described above to calculate the scores CS1 and CSq for the assessment of breast cancer risk. The proteins measured were IL-6, IL-8, VEGF, TNFα and PSA. The experiment, which included measuring approximately 300 patient samples, was divided into approximately 50% breast cancer cases diagnosed by biopsy, and 50% patients presumed to have no disease (or in this case, no breast cancer). In this group, the biopsy results of 200 samples were precisely divided into 50% non-disease and 50% with breast cancer disease, and each group was subdivided into specific age groups.

[0135] The sample...

example 2

[0145] Example 2 : Improving diagnostic accuracy using the metavariable 'age'.

[0146] Table 2 shows the statistical results of the clinical study for 868 samples of breast cancer subjects. Table 3 shows a comparison of various methods for correlation calculations. The standard method, logistic regression, only showed 82% predictive power. Standard proximity cluster analysis improved this, yielding about 88% predictive power. The method described in the specification yields greater than 97% predictive power using metavariables and weighting methods, topological stability adjustments, immune system response groupings and weighted adjustments for assay performance, combined with blinded instability testing and inconsistency Algorithm correction.

example 3

[0147] Example 3 : Improving Diagnostic Accuracy in Ovarian Cancer Research Using the Metavariable 'Age'.

[0148] Table 4 shows the results of a study of 107 women with or without ovarian cancer using the metavariable approach described herein. This study did not use all of the improvements in predictive power described in this specification, but still achieved a relatively superior predictive power of about 95%.

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Abstract

Methods for improving clinical diagnostic tests are provided, along with associated diagnostic techniques.

Description

[0001] This application is a divisional application of Chinese Patent Application No. 201480024982.6 entitled "Method for Improving Disease Diagnosis Using Detected Analytes" filed on March 13, 2014. technical field [0002] The present invention relates to methods of improving the accuracy of disease diagnosis, and to associated diagnostic tests that include the correlation of measured analytes to binary outcomes. Background technique [0003] Correlation methods in which three or more independent variables are used to correlate binary outcomes (such as the presence or absence of a given disease) are often used with cluster or neighborhood search methods, regression methods, and wavelet analysis. In the case of disease prediction, common constituents of blood or serum are measured and correlations are attempted using these concentrations as independent variables for various disease state predictions. In the case of a given disease state where the outcome is "disease" or "no...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/20G16H50/30G16B20/00G16B40/00
CPCG16H50/30G16H50/20G16B20/00G16B40/00G06N20/00
Inventor 加琳娜·克拉西克穆森·马雷法特基思·林根费尔特
Owner OTRACES INC