Classifications

• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
  • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
  • G01N33/483—Physical analysis of biological material
  • G01N33/487—Physical analysis of biological material of liquid biological material
  • G01N33/49—Blood
  • G01N33/4905—Determining clotting time of blood
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
  • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
  • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
  • G01N33/86—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving blood coagulating time or factors, or their receptors
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N2800/00—Detection or diagnosis of diseases
  • G01N2800/22—Haematology
  • G01N2800/224—Haemostasis or coagulation
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N2800/00—Detection or diagnosis of diseases
  • G01N2800/22—Haematology
  • G01N2800/226—Thrombotic disorders, i.e. thrombo-embolism irrespective of location/organ involved, e.g. renal vein thrombosis, venous thrombosis
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N2800/00—Detection or diagnosis of diseases
  • G01N2800/38—Pediatrics
  • G01N2800/385—Congenital anomalies
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
  • G16H10/40—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
  • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H70/00—ICT specially adapted for the handling or processing of medical references
  • G16H70/60—ICT specially adapted for the handling or processing of medical references relating to pathologies

Definitions

• Thrombosis and hemostasis testing involves the in vitro study of the ability of blood to form clots and to dissolve clots in vivo.
• a variety of coagulation (hemostasis) assays are used to identify congenital or acquired disorders of the coagulation system and to monitor the administration of therapeutic drugs.
• Two assays, the PT and APTT are widely used to screen for abnormalities in the coagulation system, although several other screening assays can be used, e.g. protein C, fibrinogen, protein S and/or thrombin time.
• These assays usually measure clot time, the time required to initiate clot formation following the addition of a coagulation activating agent to blood or plasma.
• PT also use the a plitude of the change in optical signal to estimate fibrinogen concentration.
• Automated methods determine clot time based on changes in electromechanical properties, clot elasticity, light scattering, fibrin adhesion, impedance or other properties. For light scattering methods, data is gathered that represents the transmission of light through the specimen as a function of time (one example of an optical time-dependent measurement profile) .
• Blood coagulation is affected by administration of drugs, in addition to the vast array of internal factors and proteins that normally influence clot formation.
• heparin is a widely-used therapeutic drug that is used to prevent thrombosis following surgery or under other conditions, or is used to combat existing thrombosis.
• the administration of heparin is typically monitored using the APTT assay, which gives a prolonged clot time in the presence of heparin.
• Clot times for PT assays are affected to a much smaller degree since a number of plasma abnormalities or therapeutic conditions may cause a prolonged APTT result, one or several additional tests are needed to isolate the exact source of the abnormality. The ability to discriminate between these effectors from screening assay results may be clinically significant.
• the present invention was conceived of and developed for presenting the relationships between an unknown sample and samples from known populations.
• the invention is intended to facilitate analysis of information embedded in the data from coagulation assays that is not included in the conventional data analysis for these assays.
• the additional information can help discriminate between underlying conditions and aid in the identification of otherwise undetected conditions .
• the present invention is directed to a method for presenting the relationship between data from an assay relating to thrombosis-hemostasis on an unknown sample, and data from a plurality of assays relating to thrombosis-hemostasis from known sample populations, including:
• step (e) creating a topological feature map of the sets of predictor variables from step (c) of the known samples in step (a) whose spatial locations within the map correspond to intrinsic features of the sets of predictor variables;
• step (h) computing the standard deviation for each predictor variable in step (c) of the known samples in step (a) ; (i) determining the z-score of the unknown sample in (b) for each predictor variable, and determining if one or more of the z-scores for the unknown sample is greater than a predetermined limit, signifying that the unknown sample is different from the known population represented by the model.
• Figure 1 is an optical profile with first and second derivatives of a normal clotting sample
• Figure 2 is a chart listing examples of predictor variables for use in the present invention.
• Figure 3 shows SOM contour plots derived from APTT optical data for six specimen categories; (1) normal donors, (2) heparinized samples, (3) specimens with elevated fibrinogen, (4) specimens with low fibrinogen, (5) specimens from patients receiving oral anticoagulants, and (6) specimens with low factor concentration (Factor II, V,VII, VIII, IX, X, XI, or XII);
• Figure 4 shows SOM contour plots derived from PT optical data for six specimen categories (1) normal donors, (2) heparinized samples, (3) specimens with elevated fibrinogen, (4) specimens with low fibrinogen, (5) specimens from patients receiving oral anticoagulants, and (6) specimens with low factor concentration (Factor II, V, VII, VIII, IX, X, XI, or XII)
• Figure 5 is an ROC plot for identification of * normal" (negative) and * abnormal" (positive) samples using a PT clot time alone and using all predictor variables (if one or more predictor variables is outside x SD' s of the normal range, than the sample is considered 'positive", where x is lsd, 2sd, 3sd, etc).
• Figure 6 is an ROC plot for identification of "normal” (negative) and "abnormal” (positive) samples using an APTT clot time alone and using all predictor variables (if one or more predictor variables is outside x SD' s of the normal range, than the sample is considered “positive", where x is lsd, 2sd, 3sd, etc).
• Figure 7 is a diagram illustrating key aspects of the present invention.
• time-dependent measurements are performed on an unknown sample (103) .
• the term "time- dependent measurement” is referred to herein to include (but is not limited to) measurements derived from assays (e.g. PT, APTT, fibrinogen, protein C, protein S, TT, and factor coagulation-based assays) .
• unknown sample refers to a sample, such as one from a medical patient (100), where a congenital or acquired imbalance or therapeutic condition associated with thrombosis/hemostasis is not known (or, if suspected, has not been confirmed) .
• a coagulation property is measured over time so as to derive a time-dependent measurement profile.
• the time- dependent measurement is an optical measurement for deriving an optical profile corresponding to changes in light scattering and/or light absorption.
• a PT profile, a fibrinogen profile, a TT profile, an APTT profile and/or variations thereof can be provided where, an unknown sample is analyzed for clot formation based on light transmittance over time through the unknown sample.
• optical measurements at two (or more) wavelengths can be taken over time so as to derive multiple optical profiles.
• two (or more) optical profiles are provided, such as both a PT profile and an APTT profile.
• the method is performed on an automated analyzer (90).
• the time-dependent measurement profile such as an optical data profile, can be provided automatically by the automated analyzer, where the unknown sample is automatically removed by an automated probe from a sample container to a test well, one or more reagents are automatically added to the test well so as to initiate the property changes within the sample which are monitored, and recorded by the analyzer.
• a set of predictor variables are defined (110) which sufficiently define the data of the time-dependent profiles.
• nine predictor variables were used.
• the optical data for a PT or APTT assay was divided into three segments (a pre-coagulation segment, a coagulation segment and a post-coagulation segment) using divisions based on the minimum and maximum value of the second derivative for changes in optical signal with respect to time.
• a model (113) is derived which represents the set of predictor variables from the known populations of specimens.
• This model can be derived from a topological feature map in one embodiment of the present invention. In another embodiment, the model is derived via a set of statistical equations.
• the model is utilized to present (120) the unknown sample's relationship to the known population (s) .
• the user may then interpret these relationships and perform confirming assays (121) .
• the output of the model (120) can be automatically stored in a memory (122) of an automated analyzer and/or displayed relative to one or more known sample populations (124) on the automated analyzer, such as on a computer monitor, or printed out on paper.
• both the derived model and other patient medical data (95) can be used for generating a model and subsequent relationship to it.
• This example demonstrates how data from known sample populations can be used to generate a topological feature map that can then be used to present the relationships between an unknown sample and known sample populations for analysis.
• a self-organizing feature map is a type of neural network that includes an input and output layer of neurons.
• the network is trained by a competitive learning algorithm where the output neurons compete with one another to be activated and only one output neuron is activated for any given set of inputs.
• the self-organizing map (SOM) algorithm transforms an input vector to an individual output neuron whose location in the output layer, or map, corresponds to features of the input data. These features tend to be spatially correlated in the map.
• SOM self-organizing map
• PT and APTT assays were performed on an automated analyzer for 765 samples. These samples represented 200 patient specimens that included normal patients, patients with a variety of deficiencies, and patients undergoing heparin or oral anticoagulant therapy.
• the 200 specimens were also tested to determine the concentration of coagulation factors (FII, FV, FVII, FVIII, FIX, FX, FXI, FXII) heparin, and fibrinogen.
• the diagnostic cut-off for defining factor deficiencies was set at 30%; that is, specimens with a measured concentration of less that 30% of normal for a specific factor were defined as deficient and those with greater than 30% activity were defined as non-deficient. Samples were defined as positive for heparin if the measured heparin concentration was greater than 0.05 IU/ml .
• a 10x10 SOM was trained using the 765 sets of nine PT predictor variables from step 4.
• a 10x10 SOM was trained using the 765 sets of nine APTT predictor variables from step 4.
• Contour plots were constructed for six categories of known specimen classifications: normal donors, specimens with heparin > 0.05 IU/ml, fibrinogen >600mg/dl, fibrinogen ⁇ 200 g/dl, patients receiving oral anticoagulants, and factor-deficient specimens (specimens with ⁇ 30% of normal activity for FII, FV, FVII, FVIII, FIX, FX, FXI, or FXII) .
• These contour plots depict the distribution of specimens within a category according to their map coordinates. The shaded areas represent the distribution of output neurons for specific specimen populations within the feature map. Each contour line represents an incremental step of one test result located at a given set of map coordinates.
• Figure 3 shows SOM contour plots derived from APTT optical data. Specimens containing low fibrinogen and high fibrinogen were classified at opposite borders of the SOM with no overlap. Normal populations showed some overlapping with low fibrinogen, factor deficient and oral anticoagulated categories. Overlap between normal specimens and edges of the high and low fibrinogen populations is expected, since some proportion of healthy donors have fibrinogen levels that are lower or higher than normal. Overlap between mapping of normal specimens and factor-deficient plasmas is also not surprising, since APTT tests are sensitive to some factor-deficiencies (but not others) , whereas PT assays are sensitive to a separate subset of factor deficiencies.
• the low fibrinogen category tended to overlap the factor-deficient category, consistent with our observation that many factor-deficient specimens also had reduced fibrinogen levels.
• the heparin category tended to overlap the high fibrinogen category, again consistent with measured levels of fibrinogen for these specimens. Little or no overlap was observed between normal specimens and specimens containing heparin. Specimens from patients receiving oral anticoagulant therapy show significant overlap with both normal and heparin populations. This is consistent with known properties of APTT assays, which are sensitive to heparin therapy but relatively insensitive to oral anticoagulant therapy. Contour plots for self-organizing feature maps trained with PT data are shown in Figure 4.
• Results are similar to maps from APTT data in several respects: (1) high and low fibrinogen were well resolved at opposite sides of the map; (2) normal specimens were localized in a region that overlapped low fibrinogen specimens slightly; (3) factor-deficient specimens were distributed between non-overlapping regions and regions that overlapped low fibrinogen and normal populations. Overlap was consistent with measured fibrinogen for some specimens, and with poor sensitivity of PT reagents to some factor deficiencies in other cases; (4) oral anticoagulated specimens showed some overlap with both normal and heparin populations; and (5) the heparinized population was distributed over a large portion of the map. Overlap between heparinized specimens and high fibrinogen populations was consistent with measured fibrinogen levels.
• the group of abnormals included various factor deficiencies, oral-anticoagulated specimens, suspected DIC specimens, and heparinized specimens .
• steps 1 through 3 were repeated for PT and APTT clot times.
• Sensitivity and specificity for non-specific abnormals as a group is higher when using all parameters rather than the traditional clot time used alone.

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• 1999
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