A MACHINE LEARNING ALGORITHM FOR PREDICTING CLINICAL OUTCOMES AND IDENTIFYING DRUG TARGETS IN ISCHEMIC STROKE
20230134886 · 2023-05-04
Inventors
- Keith R. Pennypacker (Lexington, KY, US)
- Justin F. Fraser (Lexington, KY, US)
- Qiang Cheng (Lexington, KY, US)
Cpc classification
G16B25/10
PHYSICS
A61B5/145
HUMAN NECESSITIES
G16B20/20
PHYSICS
G01N2800/2871
PHYSICS
G16B40/10
PHYSICS
C12Q1/6883
CHEMISTRY; METALLURGY
G16H50/30
PHYSICS
International classification
G16H50/30
PHYSICS
C12Q1/6883
CHEMISTRY; METALLURGY
G16B25/10
PHYSICS
Abstract
The presently-disclosed subject matter generally to methods for identifying and analyzing biomarkers to determine group effects. The presently-disclosed subject matter also relates to methods for identifying genes and proteins that increase or decrease in response to ischemic stroke damage. The disclosed subject matter further describes methods of predicting edema and infarct volume in a patient. Also described herein are methods of determining the necessity of intervention for smokers. Further disclosed herein, are methods for testing therapies for ischemic stroke.
Claims
1-15. (canceled)
16. A method of identifying risk of larger cerebral edema or infarct volume in a subject, comprising: a) obtaining a sample comprising systemic blood from the subject; b) detecting IFNA2, CCR4, IL5, IL9, IL7, and CCR3 gene products in the sample; c) identifying risk of cerebral edema in the subject when IFNA2, CCR4, IL5, IL9, IL7, CCR3 gene products are detected in the sample.
17. The method of claim 16, and further comprising isolating plasma from the sample for use in detecting the gene products.
18. The method of claim 16, wherein the subject is a stroke patient who has undergone mechanical thrombectomy (MT).
19. The method of claim 18, wherein the systemic blood sample is peripheral blood collected just proximal to a thrombus removed by the MT.
20. The method of claim 18, and further comprising obtaining a proximal sample comprising blood collected proximal to a thrombus in the subject, and a distal sample comprising blood collected distal to the thrombus in the subject.
21. The method of claim 20, and further comprising detecting IFNA2, CCR4, IL5, IL9, IL7, CCR3 gene products in the proximal sample and the distal sample, and predicting larger edema or infarct volume when there are increased amounts of IFNA2, CCR4, IL5, IL9, IL7, CCR3 gene products in the distal sample as compared to the proximal sample.
22. The method of any one of claim 18, and further comprising isolating plasma from the sample for use in detecting the gene products.
23. The method of claim 1, and further comprising administering a treatment to the subject.
24. The method of claim 23, wherein the treatment is useful for mitigating cerebral edema or infarct volume.
25. The method of claim 1, and further comprising using a device to detect the gene products in the sample, wherein the device comprises a combination of probes, comprising a probe specific for each of IFNA2, CCR4, IL5, IL9, IL7, and CCR3 gene products.
26. A device for use in identifying risk of larger cerebral edema or infarct volume in a subject, comprising a combination of probes, comprising a probe specific for each of IFNA2, CCR4, IL5, IL9, IL7, and CCR3 gene products.
27. The device of claim 26, wherein the device is a PCR array or gene array plate.
28. The device of claim 26, and further comprising a probe specific for a housekeeping gene.
29. The device of claim 28, wherein the housekeeping gene is selected from the group consisting of ACTB, B2M, GAPDH, HPRT1, and RPLP0.
Description
BRIEF DESCRIPTION OF THE DRAWINGS
[0023] The presently-disclosed subject matter will be better understood, and features, aspects and advantages other than those set forth above will become apparent when consideration is given to the following detailed description thereof. Such detailed description makes reference to the following drawings, wherein:
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[0042] While the disclosure is susceptible to various modifications and alternative forms, specific embodiments thereof have been shown by way of example in the drawings and are herein described below in detail. It should be understood, however, that the description of specific embodiments is not intended to limit the disclosure to cover all modifications, equivalents and alternatives falling within the spirit and scope of the disclosure as defined by the appended claims.
DESCRIPTION OF EXEMPLARY EMBODIMENTS
[0043] The details of one or more embodiments of the presently-disclosed subject matter are set forth in this document. Modifications to embodiments described in this document, and other embodiments, will be evident to those of ordinary skill in the art after a study of the information provided in this document. The information provided in this document, and particularly the specific details of the described exemplary embodiments, is provided primarily for clearness of understanding and no unnecessary limitations are to be understood therefrom. In case of conflict, the specification of this document, including definitions, will control.
[0044] While the terms used herein are believed to be well understood by those of ordinary skill in the art, certain definitions are set forth to facilitate explanation of the presently-disclosed subject matter.
[0045] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as is commonly understood by one of skill in the art to which the invention(s) belong.
[0046] All patents, patent applications, published applications and publications, GenBank sequences, databases, websites and other published materials referred to throughout the entire disclosure herein, unless noted otherwise, are incorporated by reference in their entirety.
[0047] Where reference is made to a URL or other such identifier or address, it understood that such identifiers can change and particular information on the internet can come and go, but equivalent information can be found by searching the internet. Reference thereto evidences the availability and public dissemination of such information.
[0048] As used herein, the abbreviations for any protective groups, amino acids and other compounds, are, unless indicated otherwise, in accord with their common usage, recognized abbreviations, or the IUPAC-IUB Commission on Biochemical Nomenclature (see, Biochem. (1972) 11(9):1726-1732).
[0049] Although any methods, devices, and materials similar or equivalent to those described herein can be used in the practice or testing of the presently-disclosed subject matter, representative methods, devices, and materials are described herein.
[0050] Following long-standing patent law convention, the terms “a”, “an”, and “the” refer to “one or more” when used in this application, including the claims. Thus, for example, reference to “a biomarker” includes a plurality of such biomarkers, and so forth.
[0051] Unless otherwise indicated, all numbers expressing quantities of ingredients, properties such as reaction conditions, and so forth used in the specification and claims are to be understood as being modified in all instances by the term “about”. Accordingly, unless indicated to the contrary, the numerical parameters set forth in this specification and claims are approximations that can vary depending upon the desired properties sought to be obtained by the presently-disclosed subject matter.
[0052] As used herein, the term “about,” when referring to a value or to an amount of mass, weight, time, volume, width, length, height, concentration or percentage is meant to encompass variations of in some embodiments ±10%, in some embodiments ±5%, in some embodiments ±1%, in some embodiments ±0.5%, and in some embodiments ±0.1% from the specified amount, as such variations are appropriate to perform the disclosed method.
[0053] As used herein, ranges can be expressed as from “about” one particular value, and/or to “about” another particular value. It is also understood that there are a number of values disclosed herein, and that each value is also herein disclosed as “about” that particular value in addition to the value itself. For example, if the value “10” is disclosed, then “about 10” is also disclosed. It is also understood that each unit between two particular units are also disclosed. For example, if 10 and 15 are disclosed, then 11, 12, 13, and 14 are also disclosed.
[0054] As used herein, “optional” or “optionally” means that the subsequently described event or circumstance does or does not occur and that the description includes instances where said event or circumstance occurs and instances where it does not. For example, an optionally variant portion means that the portion is variant or non-variant.
[0055] As used herein, the term “treatment” refers to the medical management of a patient with the intent to cure, ameliorate, stabilize, or prevent a disease, pathological condition, or disorder. This term includes active treatment, that is, treatment directed specifically toward the improvement of a disease, pathological condition, or disorder, and also includes causal treatment, that is, treatment directed toward removal of the cause of the associated disease, pathological condition, or disorder. In addition, this term includes palliative treatment, that is, treatment designed for the relief of symptoms rather than the curing of the disease, pathological condition, or disorder; preventative treatment, that is, treatment directed to minimizing or partially or completely inhibiting the development of the associated disease, pathological condition, or disorder; and supportive treatment, that is, treatment employed to supplement another specific therapy directed toward the improvement of the associated disease, pathological condition, or disorder.
[0056] As used herein, the term “prevent” or “preventing” refers to precluding, averting, obviating, forestalling, stopping, or hindering something from happening, especially by advance action. It is understood that where reduce, inhibit or prevent are used herein, unless specifically indicated otherwise, the use of the other two words is also expressly disclosed.
[0057] As used herein, the term “diagnosed” means having been subjected to a physical examination by a person of skill, for example, a physician, and found to have a condition that can be diagnosed or treated by the compounds, compositions, or methods disclosed herein. For example, “diagnosed with a stroke” means having been subjected to a physical examination by a person of skill, for example, a physician, and found to have a condition that is described as a stroke.
[0058] As used herein, the term “subject” refers to a target of administration. The subject of the herein disclosed methods can be a mammal or rodent. In some aspects of the present invention, the rodent is a rat. The term does not denote a particular age or sex.
[0059] As used herein, the terms “administering” and “administration” refer to any method of providing a pharmaceutical preparation to a subject. Such methods are well known to those skilled in the art and include, but are not limited to, oral administration, transdermal administration, administration by inhalation, nasal administration, topical administration, intravaginal administration, ophthalmic administration, intraaural administration, intracerebral administration, rectal administration, and parenteral administration, including injectable such as intravenous administration, intra-arterial administration, intramuscular administration, and subcutaneous administration. Administration can be continuous or intermittent. In various aspects, a preparation can be administered therapeutically; that is, administered to treat an existing disease or condition. In further various aspects, a preparation can be administered prophylactically; that is, administered for prevention of a disease or condition.
[0060] The term “effective amount” refers to an amount that is sufficient to achieve the desired result or to have an effect on an undesired condition. For example, a “therapeutically effective amount” refers to an amount that is sufficient to achieve the desired therapeutic result or to have an effect on undesired symptoms, but is generally insufficient to cause adverse side effects. The specific therapeutically effective dose level if or any particular patient will depend upon a variety of factors including the disorder being treated and the severity of the disorder; the specific composition employed; the age, bodyweight, general health, sex and diet of the patient; the time of administration; the route of administration; the rate of excretion of the specific compound employed; the duration of the treatment; drugs used in combination or coincidental with the specific compound employed and like factors well known in the medical arts. For example, it is well within the skill of the art to start doses of a compound at levels lower than those required to achieve the desired therapeutic effect and to gradually increase the dosage until the desired effect is achieved. If desired, the effective daily dose can be divided into multiple doses for purposes of administration. Consequently, single dose compositions can contain such amounts or submultiples thereof to make up the daily dose. The dosage can be adjusted by the individual physician in the event of any contraindications. Dosage can vary, and can be administered in one or more dose administrations daily, for one or several days. Guidance can be found in the literature for appropriate dosages for given classes of pharmaceutical products.
[0061] As used herein, the term “advanced age” refers to a subject beyond post-natal, or juvenile years. For rodents advanced age refers to a rodent that is approximately at least 15-24 months of age. In some embodiments of the instant invention, advanced age with respect to rodents refers to rodents that are at least 18 months of age.
[0062] As used herein, the term “demographics variables” refer to variables such as sex, age, race/ethnicity, BMI, comorbidities (hypertension, type 2 diabetes, hyperlipidemia, previous stroke), infarct volume, edema volume and the like.
EXAMPLES
[0063] Materials & Methods
[0064] Study Design
[0065] This study was developed from the Blood and Clot Thrombectomy Registry and Collaboration (BACTRAC) protocol: a prospectively enrolling tissue banking for emergent large vessel occlusion (ELVO) (clinicaltrials.gov NCT03153683)..sup.22 The BACTRAC study is a non-probability sampling of subjects over 18 years old treated with mechanical thrombectomy for ELVO ischemic stroke. While the BACTRAC tissue bank is continuously enrolling, data for this study was taken from subjects enrolled May 2017 through January 2019. The Institutional Review Board approved this study, and informed consent was obtained from all subjects or legally authorized representatives.
[0066] Sample Population
[0067] Subjects (≥18 years old) were eligible for inclusion if they were hospitalized with a diagnosis for an ischemic stroke and underwent thrombectomy procedure for ELVO. Subjects were candidates for inclusion if they met criteria: informed consent was signed and dated by subject or legally authorized representative within 24 h post-thrombectomy, confirmation of acute ischemic stroke based on clinical and radiographic imaging (CT and/or MRI), intra-arterial thrombectomy as determined by neurointerventional radiology (NIR) physician, an acute thromboembolus within an intracranial artery and underwent endovascular thrombectomy procedure. Subjects were excluded if baseline urine pregnancy test was positive and/or breastfeeding and/or concurrently participated in treatment studies.
[0068] Data Collection
[0069] A medical record review was conducted to collect admission clinical and demographic variables. These variables included age, sex, ethnicity, height, weight, body mass index (BMI), comorbidities, smoking status, National Institutes of Health Stroke Scale (NIHSS), baseline computerized tomography (CT) and/or magnetic resonance imaging (MRI) scan to verify large vessel occlusion stroke/thrombectomy eligibility and CTA collateral score, and time of last known normal (LKN) symptom (prior to ischemic stroke).
[0070] Tissue removal and tissue banking protocols were previously published in detail..sup.22 In brief, this was a collaborative effort among the NeuroInterventional Radiology (NIR) service line and the Center for Advanced Translational Stroke Science at University of Kentucky. At the beginning of the mechanical thrombectomy procedure, during initial access through the clot to the distal intracranial vessels, the NIR physician backbleeds the microcatheter to determine if they are distal to the distal end of the thrombus. Through this, 1 ml of whole arterial blood from the microcatheter (0.021 inch inner lumen diameter. Simultaneously, the collection of 7 ml of whole arterial peripheral blood was collected from the cervical parent artery (internal carotid artery or vertebral artery) during backbleeding while initiating suction for thrombectomy. The distal (intracranial) and proximal (peripheral) blood samples were handed off to the on-call research associates who immediately processed the specimens in the wet laboratory adjacent to the angiography suite.
[0071] An additional medical chart review was conducted to gather post-thrombectomy endpoint variables: last known normal (LKN) symptom to thrombectomy completion time (infarct time), thrombolysis in cerebral infarction (TICI) scores, and NIHSS at discharge. A blinded neuroradiologist evaluated post-thrombectomy computed tomography (CT) imaging and/or magnetic resonance imaging (MRI) for infarct and edema volumes, and hemorrhagic grade. Clinical and demographic variables, along with post-thrombectomy endpoints were deidentified and maintained in the institution's REDCap software.
[0072] RNA Extraction and Amplification
[0073] The extraction of total RNA from the cellular pellet/buffy coat was collected from the stored distal and proximal arterial blood samples using the Nucleospin Blood Kit (Macherey-Nagel, Duren, Germany), per manufacturer protocol. Nanodrop Lite Spectrophotometer (Thermo-Fisher Scientific; Waltham, Mass.) estimated the quantity of total RNA based on the A260/A280 ratio. To validate total RNA, RNA integrity numbers (RIN) values were obtained from the hospital's Genomics Core facility. RNA integrity was assessed using the Eukaryote Total RNA Nano assay on the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, Calif.). After analysis, RIN values were calculated using the expert software associated with the Agilent 2100 Bioanalyzer. Mean RIN values (±SD) for distal arterial blood were 8.89±0.41 (range from 8.30 to 9.60). Mean RIN values (±SD) for proximal arterial blood were 6.98±0.29 (range from 6.60 to 7.50). The RIN values indicate moderate to high quality RNA with minimal degradation..sup.23 RT.sup.2 First Strand Kit (Qiagen) was used to synthesize cDNA which were analyzed for gene expression using Qiagen RT.sup.2 Profiler PCR Array Human Inflammatory Cytokines & Receptors for 84 genes associated with inflammation and evaluated with StepOnePlus Real-Time PCR System interfaced with StepOne software (Applied Biosystems, Foster City, Calif.). Each gene array plate had five housekeeping genes (ACTB, B2M, GAPDH, HPRT1, and RPLP0) to normalize the gene expression data by using ΔΔCT method. The distal blood data for each gene was compared to proximal blood data for each subject. Proximal blood sample were used as a control, since there is not a true control.
[0074] Data Analysis
[0075] Descriptive and frequency statistics were obtained and used to characterize male and female ischemic stroke subjects. Comparisons were made for subject demographics and baseline characteristics using SPSS, version 26 software (IBM, Armonk, N.Y.). Statistical significance (p-value of 0.05) for group differences were assessed by independent t-tests for continuous variables and Chi-square or Fisher exact tests of associations based on categorical (ordinal and binary) variables. The gene expression mean fold change values were assessed with Scikit-learn Python package (v0.21.2) for machine learning analysis..sup.24 Statesmodels Python package (v0.10.1) was used for ordinary least squares (OLS) regression analysis..sup.25
[0076] Machine Learning
[0077] For machine learning analysis, the demographics data used included variables: sex, age, race/ethnicity, BMI, comorbidities (hypertension, type 2 diabetes, hyperlipidemia, previous stroke and atrial fibrillation), and two stroke outcomes (infarct and edema volumes). The gene expression dataset included 84 inflammatory genes. Any variables in the two stroke outcome datasets were dropped if the proportion of missing values were more than 40%. After pre-processing 89 variables were in the final dataset.
[0078] Predicting Infarct Volume
[0079] Predictive analysis was performed by using the infarct volume as the response variable, and all gene expression variables as predictors. The analysis was adjusted by using demographic variables as covariates. Regression was used to perform the predictive analysis task. Since there was a relatively small sample size with a mild number of predictors (i.e., 28 subjects with 89 predictors), a widely used machine learning method, Random Forest (RF).sup.26 27 was employed to complete the analysis. This method was used because of the reliable performance on small sample datasets with mixed categorical and continuous variables.
[0080] RF is an ensemble of decision trees, where each internal node represents a condition, or test, on a single predictor, aiming to split the dataset into two so that each had a similar response values. RF provides versatile, accurate, and stable predictions for supervised machine learning tasks such as regression as well as measuring the relative importance of each predictor. The importance score of a predictor is calculated by how much the variance is reduced or information gain is increased across all nodes that were used. The importance scores were scaled so the sum equals one.
[0081] Hyper-parameters of RF were selected in a coarse-to-fine approach using k-fold cross-validation (CV), which partitions the training dataset into k parts evenly, then rotationally uses k−1 parts to train the machine learning model and tests the model with the remaining one part. The parameters were first selected using the 5-fold CV, then tuned the parameters around the selected value using the 10-fold CV. Predicting performance of RF was optimized in mean squared error (MSE), which is minimized for the optimal model.
[0082] With the selected hyper-parameters, the importances of features using RF were ranked. Ranking may be affected by randomness of RF due to the correlations between some features. Therefore, RF was programed 100 times and the mean values of the feature importances were collected. The number of possible orders of the features is combinatorically large, this approach can effectively reduce but cannot fully eliminate the effect of the correlation between features.
[0083] Predicting Edema Volume
[0084] Similar to the prediction of infarct volumes, this was a regression task by using edema volumes as a response variable and the remaining gene variables as predictors. The analysis was adjusted using the demographic variables as covariates as done for the analysis of infarct volumes. Using similar preprocessing and cross validation with RF, the negative MSE was used as a score and maximized for optimal RF model. The relative importances of the predictors were also obtained for this task.
[0085] Stroke Outcomes of Infarct and Edema Volumes
[0086] Noncontrast head CT and CTA of the head and neck were obtained upon presentation to the emergency department during initial assessment for acute ischemic stroke. Siemens SOMATOM Definition Edge and SOMATOM Force CT scanners were used for all CT studies. CTA collateral scores were determined using maximum intensity projection images from CTA of the head with a scoring system described in previous work by Souza et al..sup.28 MRI and CT of the head without contrast were obtained following thrombectomy. MRI was performed using Siemens MAGNETOM Aera and MAGNETOM Skyra machines at magnetic field strength of 1.5 and 3.0 Tesla, respectively. Hemorrhage grade, infarct volume, and edema volume were determined on post-thrombectomy MRI of the head or CT of the head if MRI was unavailable. Hemorrhage grade was determined using a grading scale described by Hacke, et al..sup.29 Infarct volumes and edema volumes were calculated using post-thrombectomy MRI, or using CT if MRI was unavailable. Imaging included the entire brain on both MRI and CT examinations, and all images/slices were visually assessed for infarction or edema. When MRI was available, diffusion weighted images (DWI) were used to calculate infarct volumes, and T2 FLAIR images were used to calculate edema volumes. The areas of abnormal signal (restricted diffusion on DWI or hyperintense signal on T2 FLAIR) were manually segmented and analyzed to determine volume using ITK-SNAP software (www.itksnap.org)..sup.30 When MRI was unavailable, CT of the head was used to calculate both infarct and edema volume. As infarct and edema were indistinguishable on CT, the same value was used for both after manually segmenting the area of abnormal hypoattenuation using ITK-SNAP. All imaging assessment, including hemorrhage grade, CTA collateral score, infarct volume, and edema volume was performed by an experienced neuroradiologist in a blinded fashion.
Results
Example 1: Sample Characteristics
[0087] Twenty-eight subjects underwent mechanical thrombectomy during the study period. The mean age of the subjects were 66±15.48 years, primarily female (61%), and the majority were Caucasian (86%). Table 1 presents baseline subject characteristics and comparisons based on sex. Similarities were exhibited between the subjects for BMI, comorbidities (hypertension, atrial fibrillation, diabetes, hyperlipidemia, previous stroke, chronic obstructive pulmonary disease, and coronary artery disease), smoking status, hemorrhagic grade, CTA collateral scores, infarct and edema volumes. The NIHSS on average went from admission score of 17±6.56 to discharge score of 11±9.54 post-thrombectomy. More males (81.8%) than females (41.2%) exhibited full perfusion to the occluded artery post-thrombectomy with a TICI score of 3 (p=0.034). The mean infarct time (LKN to thrombectomy completion time) for males was approximately 7 hours versus almost 10 hours for females (p=0.093).
TABLE-US-00001 TABLE 1 Baseline Demographics and Characteristics for Ischemic Stroke Subjects Males (n = 11) Females (n = 17) p value Age (Years) 68 ± 17.07 64 ± 14.77 0.606 Ethnicity African-American 2 (18.2) 0 (0.0) 0.072 Caucasian 9 (81.8) 15 (88.2) 0.650 Unknown 0 (0.0) 2 (11.8) 0.254 BMI Under/Normal Weight 4 (36.4) 9 (52.9) 0.409 Overweight 6 (54.5) 6 (35.3) 0.333 Obese 0 (0.0) 0 (0.0) — Morbidly Obese 1 (9.1) 2 (11.8) 0.831 Comorbidities Hypertension 4 (36.4) 11 (64.7) 0.346 Atrial Fibrillation 3 (27.3) 5 (29.4) 0.907 Diabetes 1 (9.1) 6 (35.3) 0.127 Hyperlipidemia 3 (27.3) 2 (11.8) 0.313 Previous Stroke 2 (18.2) 3 (17.6) 0.973 COPD 1 (9.1) 3 (17.6) 0.545 CAD 3 (27.3) 1 (5.9) 0.123 Smoking Status Never 5 (45.5) 11 (64.7) 0.333 Currently 4 (36.4) 4 (23.5) 0.481 Previously (>6 months) 2 (18.2) 2 (11.8) 0.650 NIHSS on Admission 17 ± 8.04 16 ± 5.65 0.732 Minor Stroke (1-4) 2 (18.2) 1 (5.9) 0.322 Moderate Stroke (5-15) 3 (27.3) 6 (35.3) 0.671 Moderate/Severe (16-20) 2 (18.2) 6 (35.3) 0.346 Severe Stroke (≥21) 4 (36.4) 4 (23.5) 0.481 NIHSS at Discharge 14 ± 14.21 9 ± 7.32 0.249 Minor Stroke (1-4) 4 (36.4) 6 (35.3) 0.808 Moderate Stroke (5-15) 4 (36.4) 7 (41.2) 0.818 Moderate/Severe (16-20) 1 (9.1) 2 (11.8) 0.838 Severe Stroke (≥21) 2 (18.2) 1 (5.9) 0.322 TICI Score 2A =< 50% Perfusion 0 (0.0) 1 (5.9) 0.432 2B => 50% Perfusion 2 (18.2) 9 (52.9) 0.070 3 = Full Perfusion 9 (81.8) 7 (41.2) 0.034 LKN to Thrombectomy 416 ± 231.88 582 ± 249.06 0.093 Completion Time (minutes) Infarct Volume (cm.sup.3) 87.90 ± 104.58.sup. 48.33 ± 62.30 0.219 Edema Volume (cm.sup.3) 98.60 ± 116.01.sup. 48.52 ± 61.23 0.147 Hemorrhagic Grade None 4 (36.4) 4 (23.5) 0.481 HI1 4 (36.4) 7 (41.2) 0.808 H12 1 (9.1) 6 (35.3) 0.127 PH1 1 (9.1) 0 (0.0) 0.220 PH2 1 (9.1) 0 (0.0) 0.220 CTA Collateral Score 0 3 (27.3) 2 (11.8) 0.650 1 5 (45.5) 11 (64.7) 0.333 2 3 (27.3) 4 (23.5) 0.748 Values are mean ± SD or (%). Comparisons were performed with independent t-tests, Chi-square or Fisher exact tests based on distribution of data.
Example 2: Prediction of Infarct Volume with Machine Learning
[0088] For predicting infarct volume, the covariates including all 84 genes and the 10 demographic variables are all ranked in importance scores. For the ranked top k, k=1 to 84, covariates 5-fold CVs were performed to find the optimal RF model and obtain the corresponding optimal performance, which is scored in negative MSE. The importance values of the top 10 covariates, 10 for predictive analysis of infarct volume are given in Table 2. They are in descending order as follows: CCR4, IFNA2, IL-9, CXCL3, age, type 2 diabetes, IL-7, CCL4, BMI, IL-5, CCR3, TNFα, and IL-27.
TABLE-US-00002 TABLE 2 Importance values of top 10 variables for predicting infarct volume. CCR4 0.077 IFNA2 0.063 IL-9 0.053 CXCL3 0.045 Age 0.038 Diabetes 0.036 IL-7 0.034 CCL4 0.029 BMI 0.028 IL-5 0.027 CCR3 0.023 TNFα 0.022 IL-27 0.021
Example 3: Prediction of Edema Volume with Machine Learning
[0089] For predictive analysis of edema volume, in a similar way to the predictive analysis of infarct volume, the optimal performance for the top k covariates, k=1 to 84 was obtained. The importance values of the top 10 most important covariates are shown in Table 3. They are in descending order as follows: IFNA2, IL-5, CCL11, IL-17C, CCR4, IL-9, IL-7, CCR3, IL-27, type 2 diabetes, and CSF2.
TABLE-US-00003 TABLE 3 Importance values of top 10 variables for predicting edema volume IFNA2 0.074 IL-5 0.070 CCL11 0.057 IL-17C 0.045 CCR4 0.045 IL-9 0.044 IL-7 0.036 CCR3 0.035 IL-27 0.033 Diabetes 0.032 CSF2 0.025
[0090] When using infarct and edema volumes as outcome variables, selected gene subsets for these two predictive analysis had a significant overlap. The intersection of the top 10 genes from the two stroke outcomes are seven genes with type 2 diabetes, these include CCR4, IFNA2, IL-9, IL-7, IL-5, CCR3, and IL-27. This overlapping subset of genes were regarded as the most important associations at the time of mechanical thrombectomy.
Example 4: Prediction of Stroke Outcomes with Multiple Linear Regression
[0091] To validate this overlapping subset of genes, ordinary least squares (OLS) regression analysis with the top seven covariates was used. The OLS results for predictors of infarct volume and edema volume are shown in Table 4 and 5, respectively. To note, the OLS was a statistical model that assumes a linear relationship between the predictive outcome and the covariates. In contrast, the machine learning model was capable of capturing potentially highly nonlinear relationship between the outcome and the covariates..sup.31 32 Therefore, OLS may partially capture the association between the outcomes and the covariates. IL-9 and type 2 diabetes were predictors for infarct volume (F (8, 27=5.396, p=0.001). These predictors for infarct volume explained 53% of the total variance in the model (Table 4). Similarly, the OLS analysis was performed for predictors of edema volume. IL-9 was a predictor for edema volume (F (8, 27=2.938, p=0.028). IL-9 explains 33% of the total variance found in the model (Table 5).
TABLE-US-00004 TABLE 4 OLS Regression Variables Predicting Infarct Volume (n = 28) Variable β P value CCR4 −0.733 0.740 IFNa2 −0.104 0.793 IL-9 12.146 0.017 Diabetes 69.942 0.026 IL-7 −5.277 0.217 IL-5 1.205 0.233 CCR3 −0.084 0.855 IL-27 −0.683 0.549 R.sup.2 = 0.654, adjusted R.sup.2 = 0.533, df = 8, F = 5.396, p = 0.001 Durbin-Watson = 1.83
TABLE-US-00005 TABLE 5 OLS Regression Variables Predicting Edema Volume (n = 28) Variable β P value CCR4 −1.987 0.486 IFNa2 −0.099 0.848 IL-9 13.127 0.040 Diabetes 57.469 0.140 IL-7 −8.326 0.134 IL-5 2.109 0.110 CCR3 0.083 0.889 IL-27 −0.729 0.693 R.sup.2 = 0.507, adjusted R.sup.2 = 0.334, df = 8, F = 2.938, p = 0.028 Durbin-Watson = 1.69
Example 5: Gene Expression of Inflammatory Markers and Ingenuity Pathways Analysis
[0092] Ten inflammatory genes (CCR4, IFNA2, IL-9, CXCL3, IL-7, CCL4, IL-5, CCR3, TNFα, and IL-27) were recognized as predictors for infarct volume had mean fold change ranging from 2.56 to 35.06. While the inflammatory genes (IFNA2, IL-5, CCL11, IL-17C, CCR4, IL-9, IL-7, CCR3, IL-27, and CSF2) that were identified as predictors for edema volume had mean fold change ranging from 3.98 to 42.61. The seven important predictor genes (CCR4, IFNA2, IL-9, IL-7, IL-5, CCR3, and IL-27) that overlapped both stroke outcomes had mean fold change ranging from 3.98 to 35.06.
[0093] The mean fold changes for the 13 genes were analyzed through the use of Ingenuity Pathway Analysis (IPA) software (Qiagen Inc., https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis)..sup.33 IPA predicts the upstream and downstream effects of activation or inhibition of the thirteen genes from the distal blood in a pathway network. The chemokine/cytokine pattern from the distal blood overall depicts a microenvironment for chemoattraction and proliferation of immune cells, particularly in Th2 response and neutrophils (
[0094] Summary
[0095] The present invention discovered the initial inflammatory responses to ischemic stroke in stroke patients. Arterial blood samples were collected at one of the earliest timepoints (approximately 8 hours and 35 minutes from LKN to thrombectomy completion time) during an ongoing stroke. Expression of 13 cytokines and chemokines were induced over 2-fold in the distal blood relative to levels in the proximal blood. Machine learning identified 10 genes, along with age, type 2 diabetes, and BMI for predictors of infarct volume and 10 genes with type 2 diabetes for predictors of edema volume. In addition, there was an overlap with seven genes and type 2 diabetes as predictors for both stroke outcomes. These seven genes are related to the Th2 response, which is a T-cell response associated with allergy, cancer and autoimmunity..sup.34 35
[0096] Gene Expression in Distal Blood: Using the ML technique with the BACTRAC tissue bank, distal blood is isolated within the artery just immediately downstream to the clot, peripheral blood just proximal to the clot (systemic arterial blood in the cervical carotid artery), and the thrombus itself upon removal from the human subject. The blood is separated into plasma for protein analysis and into cells for mRNA isolation for gene expression analysis. BACTRAC, as a non-probability, convenience sampling of subjects (≥18 year olds) treated with mechanical thrombectomy for emergent large vessel occlusion, collects clinical and radiographic data, in addition to the tissues for testing. To understand immediate local changes in ischemic and inflammatory pathology, relative changes of gene expression in 84 inflammatory molecules in static blood distal compared to proximal systemic blood from adults who received thrombectomy for ischemic stroke were evaluated. The initial analyses of the distal and proximal blood from 28 patients revealed a dramatic increase in many inflammatory gene products related to chemoattraction and endothelial leakage. While the initial testing group was small, machine learning methods (as detailed below) were used to screen for statistically relevant changes that could predict infarct volume. Twenty-one of these mRNAs increased over 20-fold with some as high as 100- to 155-fold in distal blood relative to proximal blood.
[0097] Stroke Outcomes of Infarct and Edema Volumes and Machine Learning: MRI was performed using Siemens MAGNETOM Aera and MAGNETOM Skyra machines at magnetic field strength of 1.5 and 3.0 Tesla, respectively. Infarct volumes and edema volumes were calculated using 24-hour post-thrombectomy MRI. Diffusion weighted images (DWI) were used to calculate infarct volumes, and T2 FLAIR images were used to calculate edema volumes. The areas of abnormal signal (restricted diffusion on DWI or hyperintense signal on T2 FLAIR) were manually segmented and analyzed to determine volume using ITK-SNAP software (www.itksnap.org)..sup.29 All imaging assessment was performed by an experienced neuroradiologist in a blinded fashion. For machine learning analysis, the demographics data used two stroke outcomes (infarct and edema volumes). The gene expression dataset included 84 inflammatory mRNAs. Any variables in the two stroke outcome datasets were dropped if the proportion of missing values were more than 40%. After pre-processing, 84 variables were in the final dataset.
[0098] Predicting Infarct Volume and Edema: Predictive analyses were performed by using the infarct volume as the response variable, and all gene expression variables as predictors. Regression was used to perform the predictive analysis task. Since there was a relatively small sample size with a mild number of predictors (i.e., 28 subjects with 84 predictors), a widely used machine learning method, Random Forest (RF).sup.30,31, was employed to complete the analysis. This method was used because of the reliable performance on small sample datasets with mixed categorical and continuous variables. RF was programmed 100 times and collected the mean values of the feature importances. The number of possible orders of the features is combinatorically large, this approach can effectively reduce the effect of the correlation between features. Out of the 84 mRNAs, 10 mRNAs were determined to be significant as predictors of infarct volume as ranked by their importance values. Similar to the prediction of infarct volumes, a regression task using edema volumes as a response variable and the remaining gene variables as predictors was also performed. Ten were significant as predictors of edema according to their importance values and seven of these overlapped (TABLE 6). Next, ordinary least squares regression analysis, a statistical model that assumes a linear relationship between the predictive outcome and the covariates was performed with these seven gene products with infarct volume and edema as co-variates. In contrast, the machine learning model was capable of capturing potentially highly nonlinear relationship between the outcome and the covariates..sup.32 33 Therefore, OLS can partially capture the association between the outcomes and the covariates. This regression analysis was performed with the top seven gene covariates. This statistical method identified IL-9 expression as the only gene found to be predictor of infarct volume and edema. Despite a limited sample population, IL-9 is the strongest candidate for prediction of both infarct volume and edema in the patient population. However, RF still identified the other six gene products and their predictive role in edema/infarct volume, which can't be discounted.
TABLE-US-00006 TABLE 6 Gene Products that were predictors of both infarct volume and edema. Importance Fold Value for Importance Total Gene Induction in Infarct Value for Importance Products Distal Blood Volume Edema Values IL-9 5.1 0.053 0.044 0.097 IFNA2 35 0.063 0.074 0.137 CCR4 7.2 0.077 0.045 0.122 IL-5 9.5 0.027 0.070 0.097 IL-7 4 0.034 0.036 0.070 CCR3 16.4 0.023 0.035 0.050
[0099] Gene products identified by ML as predictors of Infarct volume/edema and their relationship to stroke: ML identified IL-27, IL-9, IFNa2, CCR4, IL-7, IL-5, and CCR3 as predictors for both infarct and edema volumes. This overlapping subset of expressed genes is regarded as the most important association at the time of mechanical thrombectomy. IL-27 is an inflammatory cytokine in the IL-12 family suggesting its signal would need to be blocked in ischemic stroke..sup.34 However, IL-27 has been shown to provide therapeutic benefit in hemorrhagic stroke,.sup.35 so this cytokine was not pursued in the instant invention given the mixed data on its relative risks/benefit in ischemia. The other 6 gene products are closely associated during the very early acute phase of stroke and are related to the Th2 response, which is a T cell response associated with allergy, cancer and autoimmunity..sup.36-38 One study has shown a systemic shift of the immune system towards Th2 responses at the late post-acute phase of stroke in human patients by examining their peripheral blood..sup.39
[0100] IL-9: Since IL-9 was identified by both ML and OLS methods as a predictor of both edema and infarct volume (Table 6), it will be the first target to be tested in the assay for stroke treatments. Tan et al. showed that IL-9 was upregulated and present in the peripheral blood mononuclear cells within 14 days after ischemic stroke in humans.sup.40. Furthermore, IL-9 has been shown to indirectly enhance blood brain barrier (BBB) permeability through VEGF-A secretion from astrocytes.sup.40 and also through eNOS production in models of cerebral ischemia.sup.41. Finally, in a brief MCAO model (1.5 hrs occlusion) in young male rats, systemic IL-9 neutralizing antibody administration significantly improved neurological function and reduced infarct volume.sup.41. Currently, one pharmacologic IL-9 inhibitor is under study (BNZ-1, Bioniz Therapeutics).
[0101] IFNa2: IFNa2 is primarily produced by dendritic cells, which are converted from macrophages when exposed to antigens from tissue damage or infection..sup.42. Elevated expression of IFNa2 has been shown to occur in vitro after oxygen-glucose deprivation.sup.43. Zhang et al. demonstrated a role of IFN signaling in worsening stroke volume and outcome in a mouse model of MCAO.sup.44. This cytokine is targeted as a treatment for systemic lupus erythematosus, an autoimmune disease.sup.45. Exposure to interferon alphas has been linked to the induction of edema.sup.46 47 and been shown to induce the production of IL-7.sup.48.
[0102] IL-7: IL-7 is required for T cell development and function.sup.49 50. The expression of IL-4, a classic marker for Th2 cells, is increased by IL-7.sup.51 and directly causes edema.sup.52. This cytokine's activity is targeted for diseases related to pathogenic T cells.sup.53.
[0103] IL-5: IL-5 is expressed and secreted by Th2 cells, and is linked to edema.sup.54. IL-5 is produced in female mice express higher levels in the spleen relative to males.sup.55. Antagonists to IL-5 have been produced for the treatment of severe asthma.sup.56.
[0104] CCR4: Th2 cells express both CCR3 and CCR4 receptors that bind chemokines to direct their migration to the site of injury or infection.sup.57 58. CCL17 and CCL22 are high affinity ligands for this receptor.sup.59 and is highly expressed in thymus and peripheral blood leukocytes, and is targeted for treatment in T-cell leukemia and peripheral T-cell lymphoma.sup.60. CCL17 that recognizes the CCR4 receptor is also expressed at elevated levels in the distal blood..sup.61
[0105] CCR3: Th2 cells express both CCR3 receptors that bind chemokines to direct their migration to the site of injury or infection. CCR3 is a chemokine receptor that has been shown to mediate neuronal injury after ischemic insult.sup.62. At least 8 different chemokines recognize this receptor and has shown that its activation enhances choroidal neovascularization and trafficking of eosinophils.sup.63. Of interest, the chemokines CCL 26, CCL11, CCL13 and CCL15 all bind to CCR3 and are expressed at high levels in the distal blood and/or thrombus (data not shown). Pharmaceutical companies have targeted this receptor for the treatment of severe asthma.sup.64.
[0106] The ML algorithm has uncovered a molecular/cellular signaling pathway that the literature supports its relationship with the facilitation of infarct volume and edema.
[0107] Use of Aged Rodents in Clinically-Relevant Experimental Stroke Studies: An aged rat MCAO model was developed to address a critical issue in stroke research: the over-reliance on young male rodents. There are reported differences in the stroke response between young and aged rodents.sup.65 66 and differences in the response between aged male and female rodents, particularly the immune response which plays a major role in stroke pathology.sup.65-67. Incorporating sex differences into studies on therapeutics is, crucial, especially since human females make up the majority of the BACTRAC human stroke study population. One important example of the failure of clinical trials related to analysis of sex is that of uric acid therapy in stroke. The trails showed that human female patients responded favorably, however, the trial ‘failed’ because it was not predesigned to evaluate the sexes individually..sup.23 It is reasonable to believe rats aged to 18 months, which is equivalent to 60 years in a human, will also function in the instant invention and males and females may be statistically treated as separate cohorts.
[0108] The MCAO model is also novel as it has been altered to simulate the human patient undergoing thrombectomy, followed by the use of the thrombectomy procedure for intra-arterial treatment delivery. The model is now modified for a 5-hour occlusion time with clinical assessment, followed by effective reperfusion and IA drug delivery. While previous experience with IA delivery after stroke modeling was in mice,.sup.68 they cannot survive the 5-hour occlusion/reperfusion. To mimic thrombectomy, Rats were chosen for this project. In preliminary research the instant invention was successfully able to alter the model to promote survival to the 30-day endpoint, as described in
[0109] To ensure the delivery concerns were addressed, intra-arterial infusion optimization experiments were performed on both male and female rats. The goal was to create the lowest infusion rate that would effectively perfuse the MCA territory while minimizing the chance of vessel rupture and subarachnoid hemorrhage. Male and female rats, both naïve and after stroke, have different optimal rates.
[0110] Th2 response in MCAO-R model. Venous blood was removed from the jugular vein prior to and immediately after MCAO-R procedure (n=5). As with the human studies, the relative changes of inflammatory gene expression in post-MCAO-R blood relative to samples taken prior to the surgery was evaluated. A Qiagen Rat PCR Inflammatory array was utilized which is similar to the Human one used in the study. Several genes related to the Th2 response were induced in the systemic venous blood after the MCAO-R surgery shown in
[0111] Middle cerebral artery occlusion with reperfusion (MCAO-R): During the MCAO-R procedure, as previously described,.sup.73 animals are placed in an induction chamber and anesthetized with 5% isoflurane/oxygen, and then maintained with a constant flow of 2-3% isoflurane in 100% oxygen at a rate of 1 L/min. A midline vertical neck incision is made, and glass rods are used to separate the large muscle pad and the sternocleidomastoid muscle (SCM). The common (CCA), external (ECA), and internal carotid (ICA) arteries are dissected and isolated. The pterygopalatine artery is identified. The first clamp is placed on the ICA prior to the pterygopalatine origin, while the second clamp is placed on the common carotid artery (CCA). The placement of the second clamp further in the posterior direction allows more room for manipulation during the embolus insertion. The ECA is isolated, and used to access the arterial system. A 20 mm 5-0 monofilament is fed distally into the ECA and advanced through to the ICA until it reaches the ICA clamp. The first clamp is removed, and the filament is advanced then sutured in place to obtain occlusion of the ICA terminal bifurcation. The second clamp is removed with careful observation of minimal blood, followed by the removal of retraction. The animal is allowed to regain consciousness for 5 hours, and then is reanesthetized for removal of the filament and potential IA infusion.
[0112] Rodent Magnetic Resonance Imaging: MRI images are acquired on a 7T Bruker Clinscan horizontal bore system (7.0T, 30 cm, 300 Hz) equipped with a triple-axis gradient system (630 mT/m and 6300 T/m/s) with a closed cycle. Diffusion Tensor Imaging (DTI) images are acquired coronally with a fat saturated, double refocused echo planar sequence: 0.297×0.297×0.7 mm.sup.3, TR/TE 2200/34, 128×128 matrix, 3 av, 15 slices, four b=0 volumes, 256 directions with b=800, in 28:23 minutes. T2 weighted images are acquired coronally with a fat saturated, turbo spin echo sequence: 0.125×0.125×0.4 mm.sup.3 resolution, TR/TE 6000/27, 192×192 matrix, 44 slices. Rats will be anesthetized with an average of 2.25% isoflurane in oxygen, while female mice are anesthetized with an average of 1.75% isoflurane in oxygen using an MRI compatible CWE Inc. equipment (Ardmore, Pa.). The animals are held in place on a Bruker scanning bed with a tooth bar and tape. Body temperature, heart rate, and respiratory rates are continuously monitored throughout the MRI scans (SA Instruments, Inc., Stony Brook, N.Y.). The animals are maintained at 37° with a water heating system built into the scanning bed. The scanning procedure takes approximately 40 minutes per animal.
[0113] MRI processing for Infarct Volume and Edema Volume: The DTI and T2 MR images are analyzed by a blinded neuroradiologist to measure the infarct volume and edema volume. These volumes are counted and the numbers are normalized to the number of images counted to provide a per section count. The volume of brain parenchyma demonstrating restricted diffusion (infarct volume) visibly affected by cerebral edema (edema volume) is calculated by manual segmentation using ITK-SNAP software. The volume of brain parenchyma visibly affected by cerebral edema (edema volume) is calculated in a similar fashion. All MRI images are analyzed by a neuroradiologist having extensive experience with imaging data analysis for stroke research in both humans and rodents..sup.66 The neuroradiologist routinely analyzes all MRI images for human and animal stroke studies, and is blinded to all other data (including age, sex, comorbidities, and results of any tissue evaluations).
[0114] Proteomic Changes in Intracranial Blood During Human Ischemic Stroke
[0115] Sample Acquisition and Olink Analysis:
[0116] Tissue samples were obtained and processed in accordance to the previously published BACTRAC protocol (PMID 30064997). Plasma was immediately frozen at −80° C. 40 μl aliquots of plasma were randomized and placed into a 96-well plate (Starstedt, Nümbrecht, Germany), covered with Micro-Amp clear adhesion film (Thermo-Fisher Scientific), and shipped overnight on dry ice to Olink Proteomics (Olink Proteomics, Boston, Mass.). Olink ran cardiometabolic and inflammatory panels on 1 μl aliquots of plasma using a proximity extension assay (PEA) with proximal blood plasma as an internal control for each subject and distal plasma as the injury site sample.
[0117] Protein Analysis:
[0118] Protein data were analyzed as average protein expression levels in distal blood plasma as compared to average protein expression levels in proximal blood plasma. The instant invention report proteomic data that includes values below the limit of detection (LOD). Data below LOD have a higher risk to be in the non-linear phase of the S-curve which may bias some estimates. However, actual data below LOD was chosen because of the large multiplate study, where LOD is a conservative measurement. Olink suggests that using actual data may increase the statistical power and give a less skewed distribution compared to replacing data below LOD with a value. Proteomic data were also analyzed excluding those values which fell below LOD and can be found in supplemental material. For sex differences, the percent change in protein expression in the distal blood as compared to the percent change in proximal blood were compared using one-tailed, unequal variance t-tests.
[0119] Patient Characteristics:
[0120] Patient demographics are shown in Table 1. There were 25 patients included in the study, of which 15 (60%) were female and all were over the age of 18. The mean age was 62 (24-91). 10 patients had a body mass index (BMI) within the normal range, 12 fell in the overweight range, and 3 fell in the obese range. 16 (64%) patients had a history of hypertension, 4 (16%) had a history of diabetes mellitus type II, 4 (16%) patients had a history of hyperlipidemia, 6 (24%) patients had a history of previous stroke, and 1 (4%) patient had a previous myocardial infarction. 15 (60%) patients had no prior history of smoking while 7 (28%) were current smokers, and 3 (12%) were previous smokers, but had not smoked within the past 6 months. According to the National Institutes of Health Stroke Score (NIHSS), on admission, 1 (4%) of patients had a minor stroke, 9 (36%) were considered to have a moderate stroke, and 8 (32%) were considered to have a moderate/severe stroke. On discharge, 10 (40%) were considered to have a minor stroke, 11 (44%) were considered to have a moderate stroke, and 1 (4%) was considered to have a severe stroke. According to the Thrombolysis in Cerebral Infarction Score (TICI), 10 (40%) had >50% perfusion and 15 (60%) had full perfusion after thrombectomy. The mean last known normal (LKN) to thrombectomy completion time was 513±246 minutes and the mean infarct volume in mm.sup.3 was 58,172±82,284.
[0121] Cardiometabolic and Inflammatory Panels:
[0122] The cardiometabolic panel analyzed 92 protein biomarkers, including proteins involved in cellular metabolic processes, cell adhesion, immune response, and complement activation. As shown in
[0123] The inflammatory panel was an immune-assay of 92 inflammation-related protein biomarkers. As shown in
[0124] Sex Differences:
[0125] For both cardiometabolic and inflammatory panels, protein expression levels were compared in males vs. females. In the cardiometabolic panel, three proteins were found to have near-significant differences in expression between males and females: PRCP (p=0.08), PLTP (p=0.09), and IL7R (p=0.06). In the inflammatory panel, two proteins were found to have near-significant differences in expression levels: CASP-8 (p=0.07), 4E-BP1 (p=0.08), and two proteins were found to have significant differences in expression levels: IL-20RA (p=0.04) and IL4 (p=0.03).
[0126] Evaluation of blood gases in blood specimens immediately proximal and distal to occlusive thrombi in patients undergoing mechanical thrombectomy.
[0127] Ischemic stroke is a prevalent, devastating disease with high morbidity and mortality. Despite extensive research using animal models, a significant lack of understanding of this complex phenomenon remains given the difficulty of translating basic science models to relevant clinical use. In an effort to ameliorate this problem, the instant invention includes a protocol to obtain and to analyze blood immediately proximal and distal to a thrombus in patients undergoing mechanical thrombectomy
[0128] Blood samples were analyzed from the first 62 patients in the BACTRAC registry. The ratios of bicarbonate, pO2, and pCO2 of distal (intracranial) to proximal (systemic) arterial blood relative to the occlusive thrombus were plotted over time. The changes in these measures across the thrombus were also compared in smokers versus nonsmokers. Changes in blood gases over time were measured via bivariate polynomial regression analysis with 2 degrees of freedom.
[0129] The mean age of the patients was 68.9 years (25-95 years). 29 were male, 33 were female. 15 patients were current smokers (24%), 47 were non-smokers (76%), defined as never having smoked at least 6 months prior to stroke. Arterial blood gases were found to be significantly altered when plotting their ratios over time. Overall, the ratios of intracranial to systemic bicarbonate increased (0.83±0.07, P<0.001), pCO2 increased (0.81±0.07, P<0.001), and pO2 decreased (0.93±0.05, P<0.001). There were significant differences in the effect of smoking on intracranial to systemic pO2 when comparing men versus women over lengthening time of infarct. In females, smoking was found to increase intracranial to systemic pO2, but this ratio decreased in nonsmokers. This phenomenon is reversed in males. Distal to systemic pO2 decreased in male smokers but increased in male nonsmokers. There were differences over time in arterial blood gases immediately proximal and distal to thrombi in large vessel occlusive stroke. The coupled changes of bicarbonate and pCO2 suggests a compensatory acid-base process. There is a gradual decrease in distal pO2 in relation to systemic pO2 over time. Smoking appears to have opposite associations to changes in intracranial:systemic pO2 in men versus women, highlighting a potential major sex difference in how smoking affects stroke pathology.
[0130] It will be understood that various details of the presently disclosed subject matter can be changed without departing from the scope of the subject matter disclosed herein. Furthermore, the foregoing description is for the purpose of illustration only, and not for the purpose of limitation.
[0131] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference, including the references set forth in the following list:
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