CN117054669B - Diagnostic or prognostic markers, products and methods for acute ischemic stroke - Google Patents
Diagnostic or prognostic markers, products and methods for acute ischemic stroke Download PDFInfo
- Publication number
- CN117054669B CN117054669B CN202311311132.1A CN202311311132A CN117054669B CN 117054669 B CN117054669 B CN 117054669B CN 202311311132 A CN202311311132 A CN 202311311132A CN 117054669 B CN117054669 B CN 117054669B
- Authority
- CN
- China
- Prior art keywords
- acute ischemic
- thrombolysis
- gfap
- uch
- patients
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 11
- 208000032382 Ischaemic stroke Diseases 0.000 title claims description 32
- 230000002537 thrombolytic effect Effects 0.000 claims abstract description 87
- 208000006011 Stroke Diseases 0.000 claims abstract description 54
- 102100039289 Glial fibrillary acidic protein Human genes 0.000 claims abstract description 53
- 101710193519 Glial fibrillary acidic protein Proteins 0.000 claims abstract description 53
- 210000005046 glial fibrillary acidic protein Anatomy 0.000 claims abstract description 53
- 102100025038 Ubiquitin carboxyl-terminal hydrolase isozyme L1 Human genes 0.000 claims abstract description 51
- 101710186825 Ubiquitin carboxyl-terminal hydrolase isozyme L1 Proteins 0.000 claims abstract description 51
- OPQRFPHLZZPCCH-PGMHBOJBSA-N [(z)-[5-chloro-1-[(2,5-dichlorophenyl)methyl]-2-oxoindol-3-ylidene]amino] acetate Chemical compound C12=CC=C(Cl)C=C2C(=N/OC(=O)C)/C(=O)N1CC1=CC(Cl)=CC=C1Cl OPQRFPHLZZPCCH-PGMHBOJBSA-N 0.000 claims abstract description 51
- 238000004393 prognosis Methods 0.000 claims abstract description 49
- 206010008190 Cerebrovascular accident Diseases 0.000 claims abstract description 41
- 230000001154 acute effect Effects 0.000 claims abstract description 41
- 230000002490 cerebral effect Effects 0.000 claims abstract description 41
- 230000000302 ischemic effect Effects 0.000 claims abstract description 40
- 238000001990 intravenous administration Methods 0.000 claims abstract description 28
- 210000004369 blood Anatomy 0.000 claims description 28
- 239000008280 blood Substances 0.000 claims description 28
- WQZGKKKJIJFFOK-GASJEMHNSA-N Glucose Natural products OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@@H]1O WQZGKKKJIJFFOK-GASJEMHNSA-N 0.000 claims description 13
- 239000008103 glucose Substances 0.000 claims description 13
- 230000000694 effects Effects 0.000 claims description 12
- 238000011156 evaluation Methods 0.000 claims description 12
- 210000001175 cerebrospinal fluid Anatomy 0.000 claims description 6
- 238000007477 logistic regression Methods 0.000 claims description 3
- 239000006249 magnetic particle Substances 0.000 claims description 2
- 210000004556 brain Anatomy 0.000 claims 1
- 230000000740 bleeding effect Effects 0.000 abstract description 29
- 230000009466 transformation Effects 0.000 abstract description 29
- 208000029028 brain injury Diseases 0.000 abstract description 24
- 239000003146 anticoagulant agent Substances 0.000 abstract description 16
- 230000002008 hemorrhagic effect Effects 0.000 abstract description 14
- 238000003745 diagnosis Methods 0.000 abstract description 13
- 239000003550 marker Substances 0.000 abstract description 6
- 150000003384 small molecules Chemical class 0.000 abstract description 2
- 206010061216 Infarction Diseases 0.000 description 42
- 230000007574 infarction Effects 0.000 description 42
- 208000032843 Hemorrhage Diseases 0.000 description 31
- 208000034158 bleeding Diseases 0.000 description 28
- 238000000556 factor analysis Methods 0.000 description 25
- 102000012288 Phosphopyruvate Hydratase Human genes 0.000 description 20
- 108010022181 Phosphopyruvate Hydratase Proteins 0.000 description 20
- 206010008118 cerebral infarction Diseases 0.000 description 16
- 208000026106 cerebrovascular disease Diseases 0.000 description 16
- 230000035488 systolic blood pressure Effects 0.000 description 15
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 description 14
- 238000001514 detection method Methods 0.000 description 14
- 206010020772 Hypertension Diseases 0.000 description 13
- 206010012601 diabetes mellitus Diseases 0.000 description 13
- 230000000391 smoking effect Effects 0.000 description 13
- 208000032928 Dyslipidaemia Diseases 0.000 description 12
- 208000033892 Hyperhomocysteinemia Diseases 0.000 description 12
- 208000017170 Lipid metabolism disease Diseases 0.000 description 12
- 238000002591 computed tomography Methods 0.000 description 12
- 230000003225 hyperhomocysteinemia Effects 0.000 description 12
- 238000012360 testing method Methods 0.000 description 9
- 208000029078 coronary artery disease Diseases 0.000 description 8
- 230000035487 diastolic blood pressure Effects 0.000 description 8
- 206010051290 Central nervous system lesion Diseases 0.000 description 7
- 230000002596 correlated effect Effects 0.000 description 7
- 238000012549 training Methods 0.000 description 7
- 230000007704 transition Effects 0.000 description 7
- 230000008499 blood brain barrier function Effects 0.000 description 6
- 210000001218 blood-brain barrier Anatomy 0.000 description 6
- 230000006378 damage Effects 0.000 description 6
- 230000035945 sensitivity Effects 0.000 description 6
- 238000003556 assay Methods 0.000 description 5
- 210000003169 central nervous system Anatomy 0.000 description 5
- 238000010837 poor prognosis Methods 0.000 description 4
- 208000022306 Cerebral injury Diseases 0.000 description 3
- 230000008901 benefit Effects 0.000 description 3
- 210000005013 brain tissue Anatomy 0.000 description 3
- 239000003153 chemical reaction reagent Substances 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000036541 health Effects 0.000 description 3
- 238000012795 verification Methods 0.000 description 3
- 206010018852 Haematoma Diseases 0.000 description 2
- 108010005656 Ubiquitin Thiolesterase Proteins 0.000 description 2
- 102100037587 Ubiquitin carboxyl-terminal hydrolase BAP1 Human genes 0.000 description 2
- 230000002378 acidificating effect Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 210000001130 astrocyte Anatomy 0.000 description 2
- 230000005779 cell damage Effects 0.000 description 2
- 208000037887 cell injury Diseases 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000001647 drug administration Methods 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 238000003384 imaging method Methods 0.000 description 2
- 208000014674 injury Diseases 0.000 description 2
- 230000003902 lesion Effects 0.000 description 2
- 239000006166 lysate Substances 0.000 description 2
- 210000002569 neuron Anatomy 0.000 description 2
- 210000001519 tissue Anatomy 0.000 description 2
- 206010003591 Ataxia Diseases 0.000 description 1
- 201000001320 Atherosclerosis Diseases 0.000 description 1
- 206010013887 Dysarthria Diseases 0.000 description 1
- 208000005189 Embolism Diseases 0.000 description 1
- 208000004929 Facial Paralysis Diseases 0.000 description 1
- 102000009123 Fibrin Human genes 0.000 description 1
- 108010073385 Fibrin Proteins 0.000 description 1
- BWGVNKXGVNDBDI-UHFFFAOYSA-N Fibrin monomer Chemical compound CNC(=O)CNC(=O)CN BWGVNKXGVNDBDI-UHFFFAOYSA-N 0.000 description 1
- 102000053171 Glial Fibrillary Acidic Human genes 0.000 description 1
- 108700005000 Glial Fibrillary Acidic Proteins 0.000 description 1
- 102000004157 Hydrolases Human genes 0.000 description 1
- 108090000604 Hydrolases Proteins 0.000 description 1
- 206010021143 Hypoxia Diseases 0.000 description 1
- 206010061218 Inflammation Diseases 0.000 description 1
- 102000008880 Peptidase C12, ubiquitin carboxyl-terminal hydrolases Human genes 0.000 description 1
- 108050000823 Peptidase C12, ubiquitin carboxyl-terminal hydrolases Proteins 0.000 description 1
- 102000011425 S100 Calcium Binding Protein beta Subunit Human genes 0.000 description 1
- 108010023918 S100 Calcium Binding Protein beta Subunit Proteins 0.000 description 1
- 102000003978 Tissue Plasminogen Activator Human genes 0.000 description 1
- 108090000373 Tissue Plasminogen Activator Proteins 0.000 description 1
- 208000030886 Traumatic Brain injury Diseases 0.000 description 1
- 206010046543 Urinary incontinence Diseases 0.000 description 1
- 208000036826 VIIth nerve paralysis Diseases 0.000 description 1
- 208000027418 Wounds and injury Diseases 0.000 description 1
- 230000002776 aggregation Effects 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 235000001014 amino acid Nutrition 0.000 description 1
- 150000001413 amino acids Chemical class 0.000 description 1
- 210000002565 arteriole Anatomy 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000006931 brain damage Effects 0.000 description 1
- 231100000874 brain damage Toxicity 0.000 description 1
- 102000014823 calbindin Human genes 0.000 description 1
- 108060001061 calbindin Proteins 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 235000018417 cysteine Nutrition 0.000 description 1
- XUJNEKJLAYXESH-UHFFFAOYSA-N cysteine Natural products SCC(N)C(O)=O XUJNEKJLAYXESH-UHFFFAOYSA-N 0.000 description 1
- 210000004292 cytoskeleton Anatomy 0.000 description 1
- 230000007850 degeneration Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 230000035622 drinking Effects 0.000 description 1
- 229940079593 drug Drugs 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 230000004064 dysfunction Effects 0.000 description 1
- 210000003722 extracellular fluid Anatomy 0.000 description 1
- 229950003499 fibrin Drugs 0.000 description 1
- 239000003527 fibrinolytic agent Substances 0.000 description 1
- 230000002518 glial effect Effects 0.000 description 1
- 230000034659 glycolysis Effects 0.000 description 1
- 230000007954 hypoxia Effects 0.000 description 1
- 238000003018 immunoassay Methods 0.000 description 1
- 230000001771 impaired effect Effects 0.000 description 1
- 208000015181 infectious disease Diseases 0.000 description 1
- 230000004054 inflammatory process Effects 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 210000003141 lower extremity Anatomy 0.000 description 1
- 238000007885 magnetic separation Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000001537 neural effect Effects 0.000 description 1
- 230000000955 neuroendocrine Effects 0.000 description 1
- 230000007658 neurological function Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 210000004129 prosencephalon Anatomy 0.000 description 1
- 238000011158 quantitative evaluation Methods 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 238000012552 review Methods 0.000 description 1
- 230000035807 sensation Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 210000003523 substantia nigra Anatomy 0.000 description 1
- 238000002560 therapeutic procedure Methods 0.000 description 1
- 229960000187 tissue plasminogen activator Drugs 0.000 description 1
- 238000003325 tomography Methods 0.000 description 1
- 238000000844 transformation Methods 0.000 description 1
- 230000008733 trauma Effects 0.000 description 1
- 230000009529 traumatic brain injury Effects 0.000 description 1
- 230000007306 turnover Effects 0.000 description 1
- 210000001364 upper extremity Anatomy 0.000 description 1
- 238000010200 validation analysis Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
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
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
- G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
-
- 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
- G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
- G01N33/573—Immunoassay; Biospecific binding assay; Materials therefor for enzymes or isoenzymes
-
- 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
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
- G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
- G01N2333/745—Assays involving non-enzymic blood coagulation factors
- G01N2333/75—Fibrin; Fibrinogen
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
- G01N2333/90—Enzymes; Proenzymes
- G01N2333/914—Hydrolases (3)
- G01N2333/948—Hydrolases (3) acting on peptide bonds (3.4)
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
- G01N2333/90—Enzymes; Proenzymes
- G01N2333/988—Lyases (4.), e.g. aldolases, heparinase, enolases, fumarase
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2800/00—Detection or diagnosis of diseases
- G01N2800/28—Neurological disorders
- G01N2800/2871—Cerebrovascular disorders, e.g. stroke, cerebral infarct, cerebral haemorrhage, transient ischemic event
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2800/00—Detection or diagnosis of diseases
- G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis
Abstract
The invention provides a diagnosis or prediction marker, a product and a method for acute ischemic cerebral apoplexy, belonging to the field of small molecule diagnosis. The four brain injuries comprise one or more of GFAP, UCH-L1, S100-beta and NSE. The invention discovers that four brain injuries are closely related to prognosis and bleeding transformation of patients suffering from acute ischemic cerebral apoplexy intravenous thrombolysis. The invention also discovers that the four brain injury items are combined, and can effectively predict the prognosis of the acute ischemic cerebral apoplexy intravenous thrombolytic patient and diagnose whether the acute ischemic cerebral apoplexy intravenous thrombolytic patient has hemorrhagic transformation.
Description
Technical Field
The invention belongs to the field of small molecule diagnosis, and particularly relates to a diagnosis or prediction marker, a product and a method for acute ischemic cerebral apoplexy.
Background
The acute ischemic cerebral apoplexy has the characteristics of high disabling rate, high mortality rate and high recurrence rate, and intravenous thrombolysis treatment is still the highest-recommended treatment mode for patients suffering from acute ischemic cerebral apoplexy at present. However, even with the widespread use of intravenous thrombolytic therapy, nearly half of patients still cannot benefit from it. Current treatment of acute ischemic stroke intravenous thrombolysis still faces many challenges.
Firstly, the acute ischemic cerebral apoplexy is easy to progress and worsen, and an accurate prediction means and an effective intervention means for the cerebral apoplexy worsen are lacked. Secondly, the thrombolytic drug improves the risk of bleeding transformation while treating acute ischemic cerebral apoplexy, and effectively avoids and timely discovers the bleeding transformation as an important link in venous thrombolysis treatment. Furthermore, patients receiving intravenous thrombolysis require strict medication restrictions, more numerous imaging examinations, etc. due to the higher risk of bleeding transition, and have more complex clinical procedures than conventional stroke patients. Head Computed Tomography (CT) is currently the most widely used imaging examination, and has problems of radiation exposure, inconvenient transportation, etc. for patients suffering from acute ischemic stroke. Therefore, aiming at the problems, a more accurate and convenient means is searched for predicting the prognosis of the patient and diagnosing the bleeding transformation, and the method is particularly important for improving the curative effect of venous thrombolysis of acute ischemic cerebral apoplexy.
Brain injury markers refer to detectable substances released into the cerebrospinal fluid or blood when brain tissue is damaged. Infection, trauma, hypoxia, inflammation or degeneration of the central nervous system can lead to cell damage and aggregation of breakdown products in extracellular fluid, as well as increased permeability of the blood brain barrier. These biomolecules diffuse into the cerebrospinal fluid along a concentration gradient and enter the blood through the leaking blood brain barrier, becoming a measurable indicator of brain injury. Among them, glial acidic fibrin (glial fibrillary acid protein, GFAP), ubiquitin carboxy terminal hydrolase L1 (ubiquitin carboxy terminal hydrolases L, UCH-L1), S100- β protein, and Neuron Specific Enolase (NSE) are important brain injury markers. GFAP is mainly present in the central nervous system, and is involved in the formation of the cytoskeleton and maintains its tensile strength, and its elevation reflects astrocyte damage. After the damage of the central nervous system, GFAP and lysate thereof can penetrate through the blood brain barrier to enter blood within 60 minutes, so that the GFAP and lysate thereof can be rapidly detected and become an important marker of the damage of the central nervous system. UCH-L1 is a cysteine hydrolase composed of 223 amino acids, has a molecular mass of about 24.8kD and belongs to the ubiquitin carboxyl terminal hydrolase family. It is highly specific to the neurons, especially found in the substantia nigra. When brain tissue nerve cell damage and blood brain barrier are destroyed, its content in cerebrospinal fluid or blood will increase significantly. S100-. Beta.is an acidic calbindin with a molecular weight of 21KD, produced mainly by astrocytes, and exists in the central nervous system in a dimer-active form in large quantities, and its level in blood and cerebrospinal fluid mainly reflects the degree of damage of brain tissue and blood-brain barrier. NSE is one of the enolases involved in the glycolytic pathway, and is present in neural tissue and neuroendocrine tissue, and the level of this marker in the blood is also significantly increased when the blood brain barrier is disrupted. These markers are currently mainly applied to traumatic brain injury and have not been effectively applied in patients with acute ischemic stroke.
Disclosure of Invention
The invention discloses four detection methods for cerebral injury, which are applied to diagnosis and clinical prognosis prediction of bleeding transformation after venous thrombolysis of acute ischemic cerebral apoplexy. The invention discovers that four brain injuries, namely GFAP, UCH-L1, S100-beta and NSE, are closely related to prognosis and bleeding transformation of patients suffering from acute ischemic cerebral apoplexy intravenous thrombolysis. And the four brain injury items are combined, so that the prognosis of an acute ischemic cerebral apoplexy intravenous thrombolytic patient can be effectively predicted, and whether the acute ischemic cerebral apoplexy intravenous thrombolytic patient is subjected to hemorrhagic transformation can be diagnosed.
In one aspect, the invention provides application of four brain injury items in evaluating the curative effect of venous thrombolysis of acute ischemic cerebral apoplexy.
The four brain injuries comprise one or more of GFAP, UCH-L1, S100-beta and NSE.
The evaluation of the curative effect of the venous thrombolysis of the acute ischemic cerebral apoplexy comprises the evaluation of the severity and prognosis of the illness state after the venous thrombolysis of the acute ischemic cerebral apoplexy.
The evaluation of the effectiveness of venous thrombolysis in acute ischemic stroke includes diagnosis of bleeding transitions that may lead to a poor prognosis for the patient.
On the other hand, the invention provides application of the reagent for detecting the four brain injuries in preparing a kit for evaluating the curative effect of venous thrombolysis of acute ischemic cerebral apoplexy.
In the application, the four brain injuries comprise one or more of GFAP, UCH-L1, S100-beta and NSE.
In the application, the evaluation of the curative effect of the venous thrombolysis of the acute ischemic cerebral apoplexy comprises the evaluation of the severity and prognosis of the illness state after the venous thrombolysis of the acute ischemic cerebral apoplexy.
Specifically, the threshold for GFAP evaluation of the prognostic effect of venous thrombolysis in acute ischemic stroke is 116pg/mL.
Specifically, the critical value of UCH-L1 for evaluating the prognosis effect of venous thrombolysis of acute ischemic cerebral apoplexy is 386pg/mL.
In such applications, assessing the effectiveness of venous thrombolysis in acute ischemic stroke includes diagnosing bleeding transitions that result in a poor prognosis.
Specifically, the threshold for the diagnosis of hemorrhagic transformation with GFAP is 12.66pg/mL.
Specifically, UCH-L1 has a critical value of 63.25pg/mL for bleeding transition diagnosis.
Specifically, the reagent is used for detecting the content of four brain injuries in a blood sample.
In yet another aspect, the invention provides a model for prognosis evaluation of venous thrombolysis functionality in acute ischemic stroke.
The model is constructed based on the following data: age, blood glucose, 24 hour NIHSS score, 24 hour GFAP and UCH-L1 levels after thrombolysis.
Preferably, the operation method of the model is binary logistic regression.
In yet another aspect, the invention provides a model for the diagnosis of bleeding after venous thrombolysis in acute ischemic stroke.
The model may be constructed by the content of brain lesions in cerebrospinal fluid or blood samples, preferably blood samples.
Specifically, the construction is performed by content data of any one or more of four brain lesions in a blood sample.
Preferably, the construction is performed by means of GFAP and UCH-L1 content data.
In yet another aspect, the invention also provides a module or system for running the aforementioned model.
The model comprises the model for acute ischemic cerebral apoplexy venous thrombolysis functional prognosis evaluation or the model for acute ischemic cerebral apoplexy venous thrombolysis bleeding transformation diagnosis.
The module or system includes a program for running the model.
In the present invention, the values of the four brain lesions may be detected by any of the methods known in the art, and the degree of difference in the results between these methods is insufficient to affect the results of the determination made according to the criteria of the present application.
The invention has the beneficial effects that:
the invention discloses four detection methods for cerebral injury, which are applied to diagnosis and clinical prognosis prediction of bleeding transformation after venous thrombolysis of acute ischemic cerebral apoplexy. The invention discovers that four brain injuries, namely GFAP, UCH-L1, S100-beta and NSE, are closely related to prognosis and bleeding transformation of patients with acute ischemic cerebral apoplexy and intravenous thrombolysis, and particularly, the application of S100 beta to evaluate the brain injury degree of the patients can avoid bias caused by clinical scoring, thus being a comparative quantitative table more objective evaluation index. And the four brain injury items are combined, so that the prognosis of an acute ischemic cerebral apoplexy intravenous thrombolytic patient can be effectively predicted, and whether the acute ischemic cerebral apoplexy intravenous thrombolytic patient is subjected to hemorrhagic transformation can be diagnosed.
Drawings
Fig. 1 is a graph of a clinical predictive model based on two terms of brain injury.
Fig. 2 is a graph of area under the work curve and calibration of subjects in different cohorts.
Fig. 3 is a clinical decision curve.
Detailed Description
The present invention will be described in further detail with reference to the following examples, which are not intended to limit the present invention, but are merely illustrative of the present invention. The experimental methods used in the following examples are not specifically described, but the experimental methods in which specific conditions are not specified in the examples are generally carried out under conventional conditions, and the materials, reagents, etc. used in the following examples are commercially available unless otherwise specified.
In the present invention, "ratio (OR)" refers to the ratio of the number of exposures to the number of non-exposures divided by the ratio of the number of exposures to the number of non-exposures in the control group.
In the present invention, "β value" refers to a normalization coefficient in linear regression, reflecting the influence of an independent variable on the magnitude of a dependent variable.
In the present invention, "the number of exposure persons" means the number of persons who have observed events in the population counted during the observation period. "non-exposed population" refers to the population of people counted during the observation period who have not experienced an observation event.
In the present invention, a "confidence interval" refers to a range in which an overall parameter is estimated with a certain probability or confidence level (1- α), which is generally referred to as a confidence interval or confidence interval (confidence interval, CI) of the parameter, and a predetermined probability (1- α) is referred to as a confidence level, usually 95% or 99%. In the embodiment, the result of the fourth bit interval is the result.
For purposes of the present invention, "NIHSS" refers to the national institutes of health Stroke Scale (National Institute of Health Stroke Scale). Is a quantitative evaluation tool for evaluating cerebral infarction, and NIHSS score is used for determining the severity of cerebral infarction of patients. The content comprises: consciousness level (consciousness level, consciousness level questioning, consciousness level instruction), gaze, visual field, facial paralysis, upper limb movement, lower limb movement, limb ataxia movement, sensation, language, dysarthria, neglect. The score ranges from 0 to 42 points, with higher scores indicating more severe impaired neurological function. (Kwah LK, diong J. National Institutes of Health Stroke Scale (NIHSS). J Physiother.2014; 60 (1): 61)
In the present invention, "mRS" means "modified rank score scale (modified Rankin scale). The functional prognosis of intravenous thrombolytic patients was assessed by 3 months mRS scoring (Broderick JP, adeoye O, elm J. Evolution of the Modified Rankin Scale and Its Use in Future Stroke three. Stroke. 2017;48 (7): 2007-12.). The classification was 7 grades: completely asymptomatic; although symptomatic, there is no obvious dysfunction, and all daily work and life can be completed; mild disability, not being able to complete all pre-illness activities, but not helping to be able to attend to their own daily routine; moderate disability, needs partial assistance, but can walk independently; moderately severe disabilities, can not independently walk, and needs other people to assist in daily life; severe disability, bedridden, urinary incontinence, and complete dependence on others in daily life; death.
In the present invention, bleeding transformations were assessed on the basis of post-thrombolytic head computerized tomography (Computed Tomography, CT), i.e., CT review, by ECASS typing (Hacke W, kaste M, fieschi C, toni D, lesafre E, von Kummer R, et al Intravenous thrombolysis with recombinant tissue plasminogen activator for acute hemispheric stroke The European Cooperative Acute Stroke Study (ECASS). JAMA. 1995;274 (13): 1017-25). Hemorrhagic transformation was defined as type HI1, HI2, PH1 and PH 2. HI1: small punctate bleeding along the edge of the infarct stove; HI2: sheet-like non-occupancy bleeding or multiple fused punctiform bleeding in the infarct zone; PH1: hematoma < 30% of infarct size with bleeding with slight placeholder effects; PH2: hematoma > 30% of infarct size with bleeding that is evident as a placeholder effect or bleeding away from the infarct focus.
In the present invention, "toat typing" is a typing for the cause of the onset of cerebral infarction patients, and can be classified into a large atherosclerosis type, a arteriole occlusion type, a heart-derived embolism type, an unknown cause type, and other causes type.
In the invention, the detection method of the four brain injuries comprises the following steps: the magnetic particle chemiluminescence method is an emerging analysis method combining magnetic separation technology, chemiluminescence technology and immunoassay technology.
EXAMPLE 1 predictive value of four brain lesions prognosis for patients with acute ischemic stroke intravenous thrombolysis
Four assays of brain injury were applied to 1607 total patients receiving intravenous thrombolytic acute ischemic stroke from 16 centers in Jilin province.
(1) Predictive value of GFAP for prognosis of patients with acute ischemic stroke venous thrombolysis
A total of 1028 patients received GFAP detection. As a result of single factor analysis, for patients with acute ischemic cerebral apoplexy receiving venous thrombolysis, GFAP after thrombolysis is related to a 3 month functional prognosis (mRS.ltoreq.1), the ratio (OR) 6.065 (95% trusted interval [95% CI ],4.125-8.918, p < 0.001). Further multi-factor analysis was performed to adjust age, gender, smoking, alcohol consumption, hypertension, diabetes, dyslipidemia, hyperhomocysteinemia, past cerebral infarction, systolic blood pressure, blood glucose, NIHSS, time of onset to dosing, TOAST typing, infarct site factors, and GFAP was found to be an independent predictor of 3 months functional prognosis, OR value 3.992 (95% CI,2.594-6.143, p < 0.001).
(2) Predictive value of UCH-L1 for prognosis of patients with acute ischemic cerebral apoplexy intravenous thrombolysis
A total of 1028 patients received UCH-L1 detection. As a result of single factor analysis, UCH-L1 was correlated with a 3 month functional prognosis (mRS.ltoreq.1) after thrombolysis, OR value 2.811 (95% CI,1.960-4.031, p < 0.001) for patients with acute ischemic stroke who received venous thrombolysis. Further multi-factor analysis was performed to adjust age, gender, smoking, alcohol consumption, hypertension, diabetes, dyslipidemia, hyperhomocysteinemia, past cerebral infarction, systolic blood pressure, blood glucose, NIHSS, time of onset to dosing, TOAST typing, infarct site factors, and found UCH-L1 to be an independent predictor of 3 months functional prognosis, OR value 1.848 (95% CI,1.247-2.738, p=0.002).
(3) Predictive value of S100-beta for prognosis of patients with acute ischemic cerebral apoplexy intravenous thrombolysis
A total of 1080 patients received S100-beta detection. As a result of single factor analysis, for patients with acute ischemic cerebral apoplexy who receive venous thrombolysis, S100-beta after thrombolysis is related to a 3 month functional prognosis (mRS.ltoreq.1), OR value is 2.307 (95% CI,1.617-3.292, p < 0.001). Further multi-factor analysis was performed to adjust age, gender, smoking, alcohol consumption, hypertension, diabetes, dyslipidemia, hyperhomocysteinemia, past cerebral infarction, coronary heart disease, systolic blood pressure, diastolic blood pressure, heart rate, blood glucose, baseline NIHSS, time to onset to dosing, TOAST typing, infarct site, bridging treatment factors, S100- β was found to be an independent predictor of a 3 month functional prognosis, OR value 1.806 (95% CI,1.211-2.692, p=0.002).
Patients were then divided into two groups, the dominant hemisphere and the non-dominant hemisphere, based on the location of the infarct focus, and after thrombolysis S100-beta was found to correlate with the 3 month functional prognosis (mRS.ltoreq.1) of patients with acute ischemic stroke venous thrombolysis with infarct focus in the dominant hemisphere, OR 15.210 (95% CI,7.231-31.993, p < 0.001). Multi-factor analysis, adjusted age, gender, smoking, alcohol consumption, hypertension, diabetes, dyslipidemia, hyperhomocysteinemia, past cerebral infarction, coronary heart disease, systolic pressure, diastolic pressure, heart rate, blood sugar, baseline NIHSS, time of onset to dosing, TOAST typing, infarct site, bridging treatment factors, S100- β is an independent predictor of 3 months functional prognosis, OR value 11.513 (95% CI,5.087-26.060, p < 0.001). Whereas S100- β is not associated with a 3 month functional prognosis for acute ischemic stroke venous thrombolytic patients with infarct foci in the non-dominant hemisphere. But in patients with acute ischemic cerebral stroke venous thrombolysis in both the dominant hemisphere and the non-dominant hemisphere, S100-beta is related to infarct volume (dominant hemisphere: beta value 55.835 [95% CI,40.781-70.889, p <0.001]; non-dominant hemisphere: beta value 18.912 [95% CI,6.275-31.548, p=0.002 ]). Multifactorial analysis, adjusted age, sex, smoking, alcohol consumption, hypertension, diabetes, dyslipidemia, hyperhomocysteinemia, past cerebral infarction, coronary heart disease, systolic pressure, diastolic pressure, heart rate, blood sugar, baseline NIHSS, time of onset to administration, TOAST typing, bridging treatment factors, S100-beta being an independent predictor of infarct volume in the dominant and non-dominant hemispheres (dominant hemispheres: beta value 45.191 [95% CI,29.360-61.023, p <0.001]; non-dominant hemispheres: beta value 17.980 [95% CI,5.033-30.927, p=0.007 ]).
At present, the comprehensive evaluation of cerebral apoplexy patients mainly depends on cerebral apoplexy scale scores, including NIHSS, mRS and the like. However, these scales are more suitable for assessing stroke patients with lesions located in the dominant hemisphere, whereas for patients with lesions located in the non-dominant hemisphere, there is a larger deviation in the assessment of the extent of brain damage. Therefore, finding an objective and quantifiable index to judge the degree of brain injury after venous thrombolysis has important significance. We find that S100.beta.is independently related to infarct volume in cerebral stroke patients with dominant hemispheres and non-dominant hemispheres, but is related to clinical prognosis only in cerebral stroke patients with dominant hemispheric infarcts, and can avoid bias caused by clinical scoring by using the S100.beta.to evaluate the cerebral injury degree of the patients, thus being a more objective evaluation index.
(4) Predictive value of NSE for prognosis of patients with acute ischemic stroke venous thrombolysis
A total of 1080 patients received NSE testing. As a result of single factor analysis, NSE was associated with a 3 month functional prognosis (mRS.ltoreq.1) OR 2.505 (95% CI,1.743-3.602, p < 0.001) in patients with acute ischemic stroke who received venous thrombolysis. Further multi-factor analysis was performed to adjust age, gender, smoking, alcohol consumption, hypertension, diabetes, dyslipidemia, hyperhomocysteinemia, past cerebral infarction, coronary heart disease, systolic blood pressure, diastolic blood pressure, heart rate, blood glucose, baseline NIHSS, time to onset to dosing, TOAST typing, infarct site, bridging treatment factors, and NSE was found to be an independent predictor of 3 months functional prognosis, OR 2.448 (95% CI,1.590-3.770, p < 0.001).
(5) Predictive value of four prognosis of venous thrombolysis patient of acute ischemic cerebral apoplexy by thrombolysis forebrain injury
By correlation analysis of four pre-venous thrombolytic brain lesions with patient prognosis. The four indexes of GFAP, UCH-L1, S100-beta and NSE are independently related to the functional prognosis (mRS is less than or equal to 2) of 3 months before thrombolysis.
A total of 1050 patients received UCH-L1 and NSE assays prior to intravenous thrombolysis, and after single factor analysis, UCH-L1 and NSE correlated with a 3 month functional prognosis, OR values were 2.860 (95% CI,1.986-4.118, p < 0.001), 2.111 (95% CI,1.473-3.024, p < 0.001), respectively. After adjusting age, sex, smoking, drinking, hypertension, diabetes, dyslipidemia, hyperhomocysteinemia, past cerebral infarction, systolic blood pressure, blood sugar, NIHSS, time to onset of drug administration, TOAST typing, infarct location, etc., UCH-L1 and NSE are still independent predictors of 3 months functional prognosis, OR values are 2.247 (95% CI,1.475-3.423, p < 0.001), 2.440 (95% CI,1.608-3.701, p < 0.001), respectively. 971 received GFAP and S100-beta detection, and after single factor analysis, GFAP and S100-beta correlated with a 3 month functional prognosis, OR values were 0.483 (95% CI,0.335-0.695, p < 0.001), 0.505 (95% CI,0.355-0.715, p < 0.001), respectively. After adjusting sex, age, smoking, alcohol consumption, hypertension, diabetes, past cerebral infarction, coronary heart disease, systolic pressure, diastolic pressure, heart rate, time to onset, TOAST typing, NIHSS factors, GFAP and S100- β are still independent predictors of a 3 month functional prognosis, OR values are 0.635 (95% CI,0.416-0.966, p=0.034), 0.564 (95% CI,0.377-0.839, p=0.005), respectively.
EXAMPLE 2 diagnosis of four brain lesions on bleeding transitions in patients with venous thrombolysis in acute ischemic stroke
Four assays of brain injury were applied to acute ischemic stroke patients receiving venous thrombolysis from 16 centers in Jilin province.
(1) Diagnostic value of GFAP for hemorrhagic transformation of patients with venous thrombolysis in acute ischemic stroke
A total of 1026 patients had complete CT data and received GFAP testing. It was found by single factor analysis that for acute ischemic stroke patients receiving venous thrombolysis, post-thrombolytic GFAP was associated with hemorrhagic transformation with OR value 12.489 (95% CI,4.894-31.870, p < 0.001). Further multi-factor analysis was performed to adjust age, sex, alcohol consumption, systolic blood pressure, heart rate, blood glucose, baseline NIHSS, time of onset to dosing, TOAST typing, infarct site factors, and GFAP was found to be an independent predictor of bleeding conversion, OR value 11.126 (95% CI,4.203-29.457, p < 0.001).
(2) Diagnostic value of UCH-L1 for hemorrhage transformation of acute ischemic cerebral apoplexy intravenous thrombolysis patient
A total of 1026 patients had complete CT data and received UCH-L1 detection. It was found by single factor analysis that UCH-L1 was associated with hemorrhagic transformation after thrombolysis with an OR value of 7.000 (95% CI,2.074-23.630, p=0.002) for acute ischemic stroke patients receiving venous thrombolysis. Further multi-factor analysis was performed to adjust age, gender, alcohol consumption, systolic blood pressure, heart rate, blood glucose, baseline NIHSS, time to onset of drug administration, TOAST typing, infarct site factors, and UCH-L1 was found to be an independent predictor of bleeding transition, OR value 4.684 (95% CI,1.858-11.623, p < 0.001).
(3) Diagnostic value of S100-beta for bleeding transformation of patients with acute ischemic cerebral apoplexy intravenous thrombolysis
A total of 1078 patients had complete CT data and received S100- β detection. It was found by single factor analysis that for acute ischemic stroke patients receiving venous thrombolysis, post-thrombolytic S100- β correlated with hemorrhagic transformation, OR value was 2.715 (95% CI,1.421-5.187, p=0.002). Further multi-factor analysis was performed to adjust age, gender, smoking, alcohol consumption, hypertension, diabetes, dyslipidemia, hyperhomocysteinemia, past cerebral infarction, coronary heart disease, systolic blood pressure, diastolic blood pressure, heart rate, blood glucose, baseline NIHSS, time to onset to dosing, TOAST typing, infarct site, bridging treatment factors, S100- β was found to be an independent predictor of bleeding conversion, OR value was 2.065 (95% CI,1.041-4.097, p=0.038).
Example 3 diagnostic value of GFAP in combination with UCH-L1 for hemorrhagic transformation
821 cases of data from the first hospital of Jilin university in example 2 were prepared according to 7: the ratio of 3 is randomly split into a training queue (581 cases) and a test queue (240 cases), and 205 cases of data from other 15 centers are used as verification queues. In the training cohort, we found that GFAP diagnostic bleeding transitions had a sensitivity of 90.2% and a Negative Predictive Value (NPV) of 96.84 at 12.66 pg/mL; when 63.25pg/mL is taken as a boundary value, UCH-L1 diagnosis hemorrhagic transformation has sensitivity of 90.2% and NPV of 97.41%, and we further combine the two markers, find that the combination of GFAP and UCH-L1 has higher value for diagnosing hemorrhagic transformation compared with a single marker, and the combination of GFAP and UCH-L1 is used for diagnosing hemorrhagic transformation in a training queue, so that the sensitivity of 98.04% is achieved, and the NPV can reach 98.31%.
Thereafter, GFAP in combination with UCH-L1 was applied to 240 other patients at the first hospital at Jilin university (independent sample test cohort). The sensitivity of the combined GFAP and UCH-L1 diagnostic bleeding turnover was found to be as high as 100% and NPV was found to be 100% in the test cohort.
The combination of GFAP and UCH-L1 is further popularized and applied to a verification queue formed by 205 patients from other centers of 15 Jilin provinces, and the sensitivity and NPV of the combination are both up to 100%.
The combination of GFAP and UCH-L1 is used for diagnosing hemorrhage transformation after venous thrombolysis, and has the characteristic of high sensitivity. Virtually all patients screened via the combination of GFAP and UCH-L1 did not undergo hemorrhagic transformation. These patients can exclude bleeding from transformation by the combination of GFAP and UCH-L1 without having to perform additional head CT examinations.
The GFAP and UCH-L1 combination is expected to replace head CT examination to eliminate hemorrhage transformation after thrombolysis and avoid radiation damage to patients. We further estimated that approximately 10% of patients were found to be unnecessary to receive additional head CT examinations after replacement of head CT examinations with the GFAP and UCH-L1 combination.
Example 4 predictive value of GFAP and UCH-L1 combination for prognosis
823 cases of data from the first hospital at Jilin university in example 1 were written according to 7:3 into a training queue (583 cases) and a test queue (240 cases), wherein in the training queue, we find that when 116pg/mL is taken as a boundary value, the GFAP prediction bad prognosis has 90.32% specificity and 80.80% Positive Predictive Value (PPV); when 386pg/mL is taken as a boundary value, UCH-L1 prediction bad prognosis has 93.95% specificity and 80.77% PPV, and we further combine the two markers, find that the combination of GFAP and UCH-L1 has higher value compared with the prediction of single marker bad prognosis, and the combination prediction of GFAP and UCH-L1 is applied in a training queue, so that 97.58% specificity is achieved, and the PPV can reach 88.46%. Thereafter, GFAP in combination with UCH-L1 was applied to 240 other patients at the first hospital at Jilin university (independent sample test cohort). It was found that in the test cohort, the specificity of the combined GFAP and UCH-L1 for predicting a poor prognosis was as high as 98.92% and the PPV was as high as 94.12%. The GFAP and UCH-L1 combination is further popularized and applied to a verification queue formed by 205 patients from other centers of 15 Jilin provinces, the specificity reaches 99.21 percent, and the PPV reaches 80 percent.
In addition, in the training cohort, we constructed a clinical predictive model consisting of age, blood glucose, 24 hour NIHSS score, and 24 hour post thrombolytic GFAP, UCH-L1 levels using a binary logistic regression model in SPSS software, and plotted noman plots (fig. 1).
This model was further applied in a test cohort consisting of 240 patients from the first hospital at Jilin university, which predicts that AUC values for a 3 month functional prognosis could reach 0.834 (95% CI, 0.783-0.885).
Continuing to promote the model in another 15 hospitals, in a validation cohort consisting of 205 patients, the AUC values predicted to be functional prognosis for 3 months could reach 0.824 (95% CI, 0.764-0.883). This demonstrates that this clinical predictive model, derived from four assays of brain injury, has high accuracy in predicting a 3 month functional prognosis.
We further evaluated the accuracy of the model in a number of ways. As shown in fig. 2, which is a calibration graph of the prediction model, by comparing the difference between the actual observed poor result and the poor result predicted by the model, the result shows that the model predicted result has higher consistency with the actual 3 months poor prognosis. FIG. 3 is a clinical decision curve, estimating the net benefit of the model based on the difference between the number of true and false positive results, and it can be seen that the use of predictive models based on GFAP, UCH-L1 detection results to predict the prognosis of a patient treated with intravenous thrombolysis has a higher clinical benefit.
EXAMPLE 5 predictive value of four brain lesions on acute ischemic stroke infarct volume
Four assays of brain injury were applied to acute ischemic stroke patients receiving venous thrombolysis from 16 centers in Jilin province.
(1) Prediction value of GFAP on infarct volume of acute ischemic cerebral apoplexy intravenous thrombolysis patient
A total of 942 patients measured infarct volume and received GFAP detection. As a result of single factor analysis, it was found that GFAP after thrombolysis was correlated with infarct volume for patients with acute ischemic stroke who received venous thrombolysis, and the beta value was 43.806 (95% CI,34.085-53.528, p < 0.001). Further multi-factor analysis was performed to adjust age, gender, smoking, alcohol consumption, hypertension, diabetes, dyslipidemia, hyperhomocysteinemia, past cerebral infarction, systolic blood pressure, blood glucose, NIHSS, time of onset to administration, TOAST typing, infarct site factors, and found that GFAP was an independent predictor of infarct volume with beta value 34.490 (95% CI,24.208-44.772, p < 0.001).
(2) Predictive value of UCH-L1 on infarct volume of patients suffering from acute ischemic cerebral apoplexy intravenous thrombolysis
A total of 942 patients measured infarct volume and received UCH-L1 detection. As a result of single factor analysis, UCH-L1 was found to be correlated with infarct volume after thrombolysis for patients with acute ischemic stroke who received venous thrombolysis, with beta values of 42.590 (95% CI,32.783-52.397, p < 0.001). Further multi-factor analysis was performed to adjust age, gender, smoking, alcohol consumption, hypertension, diabetes, dyslipidemia, hyperhomocysteinemia, past cerebral infarction, systolic blood pressure, blood glucose, NIHSS, time of onset to administration, TOAST typing, infarct site factors, and found UCH-L1 to be an independent predictor of infarct volume with beta value 32.782 (95% CI,22.658-42.906, p < 0.001).
(3) Prediction value of S100-beta on infarct volume of acute ischemic cerebral apoplexy intravenous thrombolysis patient
A total of 992 patients measured infarct volume and received S100-. Beta.detection. As a result of single factor analysis, it was found that, for acute ischemic stroke patients receiving venous thrombolysis, post-thrombolytic S100-beta correlated with infarct volume, the beta value was 36.716 (95% CI,27.292-46.140, p < 0.001). Further multi-factor analysis was performed to adjust age, gender, smoking, alcohol consumption, hypertension, diabetes, dyslipidemia, hyperhomocysteinemia, past cerebral infarction, coronary heart disease, systolic blood pressure, diastolic blood pressure, heart rate, blood sugar, baseline NIHSS, time to onset to dosing, TOAST typing, infarct site, bridging treatment factors, and found that S100-beta is an independent predictor of infarct volume, with beta values of 29.942 (95% CI,20.410-39.474, p < 0.001).
(4) Predictive value of NSE on infarct volume in patients with acute ischemic stroke intravenous thrombolysis
A total of 992 patients measured infarct volume and received NSE detection. As a result of single factor analysis, NSE was found to correlate with infarct volume after thrombolysis for patients with acute ischemic stroke who received venous thrombolysis, with beta values of 21.920 (95% CI,11.826-32.014, p < 0.001). Further multi-factor analysis was performed to adjust age, gender, smoking, alcohol consumption, hypertension, diabetes, dyslipidemia, hyperhomocysteinemia, past cerebral infarction, coronary heart disease, systolic blood pressure, diastolic blood pressure, heart rate, blood glucose, baseline NIHSS, time to onset to dosing, TOAST typing, infarct site, bridging treatment factors, and NSE was found to be an independent predictor of infarct volume with beta value 16.547 (95% CI,6.506-26.587, p=0.001).
Claims (1)
1. A system for running a model for 3 months functional prognosis evaluation of patients treated with venous thrombolysis in acute ischemic brain stroke, characterized in that the model is a binary logistic regression model, constructed based on the following data: age, blood glucose, 24 hour NIHSS score, 24 hour GFAP and UCH-L1 levels after thrombolysis; the GFAP and UCH-L1 levels 24 hours after thrombolysis are detected by a magnetic particle chemiluminescence method; the sample is cerebrospinal fluid or blood sample;
the critical value of the prognosis effect of the GFAP on the acute ischemic cerebral apoplexy intravenous thrombolysis treatment patient is 116 pg/mL; the critical value for assessing the prognostic effect of patients treated with venous thrombolysis for acute ischemic stroke using UCH-L1 is 386pg/mL.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311311132.1A CN117054669B (en) | 2023-10-11 | 2023-10-11 | Diagnostic or prognostic markers, products and methods for acute ischemic stroke |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311311132.1A CN117054669B (en) | 2023-10-11 | 2023-10-11 | Diagnostic or prognostic markers, products and methods for acute ischemic stroke |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117054669A CN117054669A (en) | 2023-11-14 |
| CN117054669B true CN117054669B (en) | 2024-03-29 |
Family
ID=88655727
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311311132.1A Active CN117054669B (en) | 2023-10-11 | 2023-10-11 | Diagnostic or prognostic markers, products and methods for acute ischemic stroke |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117054669B (en) |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6235489B1 (en) * | 1999-02-26 | 2001-05-22 | Syn X Pharma | Method for diagnosing and distinguishing stroke and diagnostic devices for use therein |
| US6780606B1 (en) * | 1999-02-26 | 2004-08-24 | Synx Pharma, Inc. | Method for diagnosing and distinguishing stroke and diagnostic devices for use therein |
| WO2015066211A1 (en) * | 2013-10-29 | 2015-05-07 | Banyan Biomarkers, Inc. | Uch-l1 isoforms, assays and devices for detection of a neurological condition |
| CN104677974A (en) * | 2013-12-02 | 2015-06-03 | 陈惠� | Combined diagnosis model for multiple protein markers in acute ischemic brain stroke |
| RU2573801C1 (en) * | 2015-02-13 | 2016-01-27 | Федеральное государственное бюджетное научное учреждение "НАУЧНЫЙ ЦЕНТР НЕВРОЛОГИИ" | Method for prediction of clinical course of acute ischemic stroke during 24 hours of performing thrombolytic therapy |
| CN105687350A (en) * | 2016-03-16 | 2016-06-22 | 广东药学院 | Traditional Chinese medicine composition for treating ischemic cerebral apoplexy and application thereof |
| CN111214645A (en) * | 2019-10-26 | 2020-06-02 | 沈阳医学院 | Application of GHRH-A in preparation of medicine for preventing and treating ischemic cerebral infarction |
| KR20200069665A (en) * | 2018-12-07 | 2020-06-17 | 서울대학교병원 | Prognostic score system predicting 3 month functoinal outcome following acute ischemic stroke |
| CN111739637A (en) * | 2020-06-23 | 2020-10-02 | 王凯 | Clinical diagnosis and treatment application of acute ischemic stroke based on cerebral microhemorrhage detection |
| RU2748685C1 (en) * | 2020-11-30 | 2021-05-28 | ФГБОУ ВО СибГМУ Минздрава России | Method for predicting the probability of fatal outcome in 14-day period of acute ischemic stroke of the brain |
| CN113607956A (en) * | 2020-11-04 | 2021-11-05 | 北京北方生物技术研究所有限公司 | Application of apoptosis-related plaque-spotted protein (ASC) as cerebral arterial thrombosis marker |
| CN114791498A (en) * | 2022-01-26 | 2022-07-26 | 首都医科大学宣武医院 | Application of Syndecano-1 as marker in prognosis evaluation of acute ischemic stroke venous thrombolysis patient |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8492107B2 (en) * | 2004-04-15 | 2013-07-23 | University Of Florida Research Foundation, Inc. | Neural proteins as biomarkers for nervous system injury and other neural disorders |
| CN110366558A (en) * | 2016-10-28 | 2019-10-22 | 班扬生物标记公司 | For the antibody and correlation technique of ubiquitin c-terminal hydrolase-l 1 (UCH-L1) and glial fibrillary acid protein (GFAP) |
-
2023
- 2023-10-11 CN CN202311311132.1A patent/CN117054669B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6235489B1 (en) * | 1999-02-26 | 2001-05-22 | Syn X Pharma | Method for diagnosing and distinguishing stroke and diagnostic devices for use therein |
| US6780606B1 (en) * | 1999-02-26 | 2004-08-24 | Synx Pharma, Inc. | Method for diagnosing and distinguishing stroke and diagnostic devices for use therein |
| WO2015066211A1 (en) * | 2013-10-29 | 2015-05-07 | Banyan Biomarkers, Inc. | Uch-l1 isoforms, assays and devices for detection of a neurological condition |
| CN104677974A (en) * | 2013-12-02 | 2015-06-03 | 陈惠� | Combined diagnosis model for multiple protein markers in acute ischemic brain stroke |
| RU2573801C1 (en) * | 2015-02-13 | 2016-01-27 | Федеральное государственное бюджетное научное учреждение "НАУЧНЫЙ ЦЕНТР НЕВРОЛОГИИ" | Method for prediction of clinical course of acute ischemic stroke during 24 hours of performing thrombolytic therapy |
| CN105687350A (en) * | 2016-03-16 | 2016-06-22 | 广东药学院 | Traditional Chinese medicine composition for treating ischemic cerebral apoplexy and application thereof |
| KR20200069665A (en) * | 2018-12-07 | 2020-06-17 | 서울대학교병원 | Prognostic score system predicting 3 month functoinal outcome following acute ischemic stroke |
| CN111214645A (en) * | 2019-10-26 | 2020-06-02 | 沈阳医学院 | Application of GHRH-A in preparation of medicine for preventing and treating ischemic cerebral infarction |
| CN111739637A (en) * | 2020-06-23 | 2020-10-02 | 王凯 | Clinical diagnosis and treatment application of acute ischemic stroke based on cerebral microhemorrhage detection |
| CN113607956A (en) * | 2020-11-04 | 2021-11-05 | 北京北方生物技术研究所有限公司 | Application of apoptosis-related plaque-spotted protein (ASC) as cerebral arterial thrombosis marker |
| RU2748685C1 (en) * | 2020-11-30 | 2021-05-28 | ФГБОУ ВО СибГМУ Минздрава России | Method for predicting the probability of fatal outcome in 14-day period of acute ischemic stroke of the brain |
| CN114791498A (en) * | 2022-01-26 | 2022-07-26 | 首都医科大学宣武医院 | Application of Syndecano-1 as marker in prognosis evaluation of acute ischemic stroke venous thrombolysis patient |
Non-Patent Citations (16)
| Title |
|---|
| Diagnostic Accuracy of Plasma Glial Fibrillary Acidic Protein for Differentiating Intracerebral Hemorrhage and Cerebral Ischemia in Patients with Symptoms of Acute Stroke;Christian Foerch等;Clinical Chemistry;第58卷(第1期);第237–245页 * |
| Glial fibrillary acidic protein as a prognostic marker of acute ischemic stroke;G Liu等;Human & Experimental Toxicology;第37卷;第1048-1053页 * |
| Prognostic value of day-of-injury plasma GFAP and UCH-L1 concentrations for predicting functional recovery after traumatic brain injury in patients from the US TRACK-TBI cohort: an observational cohort study;Frederick K Korley;The LANCET Neurology;20220930;第21卷(第9期);第803-813页 * |
| S100B, GFAP, UCH-L1 and NSE as predictors of abnormalities on CT imaging following mild traumatic brain injury: a systematic review and meta-analysis of diagnostic test accuracy;Michael Amoo等;Neurosurgical Review;第45卷(第2期);第1171-1193页 * |
| The prognostic value of serum pregnancy-associated plasma protein A, S100 and high sensitivity C-reactive protein in acute ischemic stroke patients without heparin administration;Lili Wang等;Clinical Biochemistry;第47卷;第187-191页 * |
| 丁苯酞联合阿替普酶治疗对急性缺血性脑卒中患者血清NSE、S100β水平的影响;王洪志等;卒中与神经疾病;第24卷(第06期);第530-533页 * |
| 急性缺血性脑卒中患者溶栓后HMGB1、hs-CRP、IL-18、NSE变化及与预后的关系;黄越等;中国实用医药;第15卷(第34期);第68-70页 * |
| 急性缺血性脑卒中患者血浆趋化因子12与白细胞介素-33和泛素羧基末端水解酶-1水平变化及意义;程启慧;中华实用诊断与治疗杂志;第32卷(第07期);第657-660页 * |
| 急性缺血性脑卒中静脉溶栓后出血转化相关危险因素的临床研究;吴展兴等;航空航天医学杂志;第31卷(第09期);第1042-1044页 * |
| 探讨应用阿替普酶静脉溶栓治疗急性缺血性脑卒中的临床疗效;董万亮等;数理医药学杂志;第34卷(第04期);第541-543页 * |
| 涂宏等主编.常见病联合用药手册.中国医药科学技术出版社,2021,(第1版),第416页. * |
| 胶质纤维酸性蛋白、泛素羧基端水解酶L1联合卒中量表评分在急性脑梗死预后评估中的应用研究;刘成等;实用医院临床杂志;20230531;第20卷(第03期);第133-136页 * |
| 血清AQP4、GFAP联合检测对急性缺血性脑卒中患者静脉溶栓后出血性转化的预测价值;汪文兵等;临床误诊误治;第35卷(第05期);第102-105页 * |
| 醒脑静注射液在急性脑出血治疗中的临床疗效和安全性;李震等;世界中医药;20190331;第14卷(第03期);第661-665页 * |
| 阿替普酶静脉溶栓治疗与非溶栓治疗对急性脑梗死患者神经功能的影响研究;陈静霞;海南医学院学报;第23卷(第02期);第244-247页 * |
| 陈静霞.阿替普酶静脉溶栓治疗与非溶栓治疗对急性脑梗死患者神经功能的影响研究.海南医学院学报.2017,第23卷(第02期),第244-247页. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117054669A (en) | 2023-11-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Santos et al. | Biomarkers of severity and threshold of allergic reactions during oral peanut challenges | |
| US11598782B2 (en) | Biomarkers and methods for detection of seizures and epilepsy | |
| Anderson et al. | Blood-based biomarkers for prediction of intracranial hemorrhage and outcome in patients with moderate or severe traumatic brain injury | |
| Kiely et al. | Validation of four measures of mental health against depression and generalized anxiety in a community based sample | |
| Gariballa et al. | Influence of nutritional status on clinical outcome after acute stroke | |
| Mondello et al. | Neuronal and glial markers are differently associated with computed tomography findings and outcome in patients with severe traumatic brain injury: a case control study | |
| JP5116485B2 (en) | Stress determination method, stress determination marker, diagnostic agent for stress determination, and stress determination system | |
| US20120149042A1 (en) | Methods for predicting outcome in traumatic brain injury | |
| Sharma et al. | A blood-based biomarker panel to detect acute stroke | |
| Chiong et al. | Development of an objective tool for the diagnosis of myxedema coma | |
| Lange et al. | Clinical utility of the protein S100B to evaluate traumatic brain injury in the presence of acute alcohol intoxication | |
| Degerli et al. | Could mean platelet volume be a reliable indicator for acute mesenteric ischemia diagnosis? A case-control study | |
| Peters et al. | Longitudinal course of traumatic brain injury biomarkers for the prediction of clinical outcomes: a review | |
| Liu et al. | Clinical significance of FSTL 1, Bax, Bcl-2 in acute cerebral infarction and its relationship with hemorrhagic transformation. | |
| CN108508211A (en) | Starting non-medication schizophrenic patients blood serum designated object FGF9 and its application | |
| Forsman et al. | Glucose metabolism in completed suicide: a forensic-pathological pilot study | |
| CN117054669B (en) | Diagnostic or prognostic markers, products and methods for acute ischemic stroke | |
| Umebachi et al. | Measurement of blood lactate, D-dimer, and activated prothrombin time improves prediction of in-hospital mortality in adults blunt trauma | |
| Trueba et al. | Effects of academic exam stress on nasal leukotriene B4 and vascular endothelial growth factor in asthma and health | |
| Bhuiyan et al. | Timing of menarche related to carotid artery intima-media thickness in black and white young adult women: the Bogalusa Heart Study | |
| Ying et al. | Serum sulfonylurea receptor-1 as a biomarker of clinical severity and prognosis in patients with traumatic brain injury | |
| Berger et al. | The use of biomarkers after inflicted traumatic brain injury: insight into etiology, pathophysiology, and biochemistry | |
| Coghe et al. | C-reactive protein levels in the first days of life: a systematic statistical approach | |
| Hawash et al. | S100B and brain ultrasound: Novel predictors for functional outcome in acute ischemic stroke patients | |
| JP2020525765A (en) | IL-10, S100B and H-FABP markers and the use of the markers in the detection of traumatic brain injury |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |