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WO2021215821A1 - Méthode de prédiction de pronostic neurologique d'un patient atteint d'un syndrome post-arrêt cardiaque - Google Patents

Méthode de prédiction de pronostic neurologique d'un patient atteint d'un syndrome post-arrêt cardiaque Download PDF

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Publication number
WO2021215821A1
WO2021215821A1 PCT/KR2021/005012 KR2021005012W WO2021215821A1 WO 2021215821 A1 WO2021215821 A1 WO 2021215821A1 KR 2021005012 W KR2021005012 W KR 2021005012W WO 2021215821 A1 WO2021215821 A1 WO 2021215821A1
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brain
region
cortex
patient
prognosis
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Korean (ko)
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윤혜전
김범산
김대희
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Industry Academic Cooperation Foundation of Catholic University of Korea
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
    • A61B6/5211—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data
    • A61B6/5217—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data extracting a diagnostic or physiological parameter from medical diagnostic data
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/48—Diagnostic techniques
    • A61B6/481—Diagnostic techniques involving the use of contrast agents
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications
    • A61B6/501—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications for diagnosis of the head, e.g. neuroimaging or craniography
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • 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
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • 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
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10116—X-ray image
    • G06T2207/10128—Scintigraphy
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30016—Brain

Definitions

  • Embodiments of the present invention relate to a method for predicting the neurological prognosis of a patient with post-cardiac arrest syndrome.
  • Cardiac arrest is a disease with a very high mortality rate, and the mortality rate due to cardiac arrest outside the hospital exceeds 90%. In addition, even if the patient is resuscitated after cardiac arrest, severe neurological damage is often experienced. The high mortality and morbidity of cardiac arrest is usually due to dysfunction of the brain and heart, a syndrome called post-cardiac arrest syndrome (PCAS).
  • PCAS post-cardiac arrest syndrome
  • CPC cerebral performance categories
  • GOS Glasgow outcome scoring
  • Embodiments of the present invention provide a method for predicting a neurological prognosis of a patient with postcardiac arrest syndrome and a computer program stored in a recording medium for executing the method.
  • this is an example, and the object of the present invention is not limited thereto.
  • the method of predicting the neurological prognosis of a patient with post-cardiac arrest syndrome includes imaging a brain image of a patient with post-cardiac arrest syndrome, and metabolic function of the brain from the captured image. Calculating the level and determining a neurological prognosis based on the calculated level of metabolic function of the brain.
  • the step of capturing the brain image comprises injecting a radiopharmaceutical into the post-cardiac arrest syndrome patient, and the radiopharmaceutical is injected into the brain
  • a standardized uptake value (SUV) of the brain may be calculated based on the captured image.
  • the calculating of the metabolic function level of the brain includes an insular cortex, an auditory cortex, and an auditory cortex among regions of the brain.
  • the capturing of the brain image may include capturing the image after a first period from the occurrence of cardiac arrest.
  • the determining of the neurological prognosis includes: when the calculated SUV of the brain is lower than the first value, the neurological prognosis is can be considered bad.
  • the determining of the neurological prognosis includes a difference between the calculated SUV of the brain and a preset SUV higher than a second value. In this case, the neurological prognosis may be judged to be poor.
  • the determining of the neurological prognosis comprises dividing the calculated SUV for each region of the brain by the SUV of the entire brain, SUVR calculating a (standardized uptake value ratio) and dividing the brain region into a first region and a second region, and when the SUVR of the first region is lower than a third value and the SUVR of the second region is a fourth
  • the method may include determining that the neurological prognosis is poor in at least any one of the cases higher than the value.
  • the determining of the neurological prognosis comprises dividing the calculated SUV for each region of the brain by the SUV of the entire brain, SUVR calculating , dividing the brain region into a third region and a fourth region, calculating a ratio of SUVR of the third region to SUVR of the fourth region, so that the calculated ratio is lower than a fifth value case, it may include determining that the neurological prognosis is poor.
  • the fifth value may be 1.22.
  • the third region may be a forebrain, and the fourth region may be a hindbrain.
  • a computer program according to another embodiment of the present invention is a computer program stored in a recording medium to execute the method of predicting a neurological prognosis of a patient with post-cardiac arrest syndrome according to an embodiment of the present invention.
  • the method for predicting the neurological prognosis of a patient with post-cardiac arrest syndrome can predict the neurological prognosis of a patient with post-cardiac arrest syndrome immediately after cardiac arrest occurs.
  • the method for predicting the neurological prognosis of a patient with post-cardiac arrest syndrome evaluates the metabolic function level of the brain from a PET image of a patient with post-cardiac arrest syndrome, and based on the evaluation of the neurological prognosis of a patient with post-cardiac arrest syndrome can be predicted accurately.
  • FIG. 1 is a diagram illustrating a system for implementing a method for predicting a neurological prognosis of a patient with post-cardiac arrest syndrome according to an embodiment of the present invention.
  • FIG. 2 is a diagram illustrating a method of predicting a neurological prognosis of a patient with post-cardiac arrest syndrome according to an embodiment of the present invention.
  • FIG. 3 is a diagram illustrating a model of a method for predicting a neurological prognosis of a patient with post-cardiac arrest syndrome according to an embodiment of the present invention.
  • FIG. 4 is a view showing the results of the Morris underwater maze test according to an embodiment of the present invention.
  • FIG. 5 shows an ROC curve of FHR according to an embodiment of the present invention.
  • FIG. 6 is a view showing a PET image and a Morris underwater maze test result according to an embodiment of the present invention.
  • the method of predicting the neurological prognosis of a patient with post-cardiac arrest syndrome includes imaging a brain image of a patient with post-cardiac arrest syndrome, and metabolic function of the brain from the captured image. Calculating the level and determining a neurological prognosis based on the calculated level of metabolic function of the brain.
  • FIG. 1 is a diagram illustrating a system 10 for implementing a method for predicting a neurological prognosis of a patient with post-cardiac arrest syndrome according to an embodiment of the present invention
  • FIG. 2 is a diagram showing post-cardiac arrest syndrome according to an embodiment of the present invention. It is a diagram showing a method of predicting a patient's neurological prognosis.
  • a system 10 for implementing a method for predicting a neurological prognosis of a patient with post-cardiac arrest syndrome includes an information processing unit 100, an imaging unit 200, and a database. part 300 may be included.
  • the information processing unit 100 may include all kinds of devices capable of processing data, such as a processor.
  • the 'processor' may refer to a data processing device embedded in hardware, for example, having a physically structured circuit to perform a function expressed as a code or an instruction included in a program.
  • a microprocessor a central processing unit (CPU), a processor core, a multiprocessor, an application-specific integrated (ASIC) circuit
  • ASIC application-specific integrated
  • FPGA field programmable gate array
  • the information processing unit 100 may calculate the metabolic function level of the brain based on the brain image of the postcardiac arrest syndrome patient received from the imaging unit 200 . Also, the information processing unit 100 may determine the neurological prognosis of the postcardiac arrest syndrome patient based on the calculated level of metabolic function of the brain.
  • the information processing unit 100 calculates the glucose metabolic function level of the brain of the postcardiac arrest syndrome patient based on the FDG-PET (fludeoxyglucose-positron emission tomography) image of the postcardiac arrest syndrome patient captured by the imaging unit 200 . can be calculated.
  • the glucose metabolic function level may be calculated as a standardized uptake value (SUV).
  • the standard uptake factor is a unit that evaluates how much higher or lower than average the radiopharmaceutical is ingested within a target area, assuming that the glucose-containing radiopharmaceutical (eg, 18F-FDG) is evenly distributed.
  • the information processing unit 100 may transmit/receive data to and from a personal terminal such as the smart phone 400-1 or the computer 400-2 through a communication network or the like. Accordingly, the information processing unit 100 may receive information about the patient through the personal terminal, and may provide the determined neurological prognosis of the patient with post-cardiac arrest syndrome through the personal terminal.
  • the imaging unit 200 captures a brain image of a patient with post-cardiac arrest syndrome.
  • the imaging unit 200 may be a device that captures a nuclear medicine image. More specifically, the imaging unit 200 may be an FDG-PET device capable of confirming the extent to which 18F-fludeoxyglucose (18F-FDG), which is a glucose-like substance as a radiopharmaceutical, is absorbed into the brain.
  • the imaging unit 200 may use 18F-FDG injected into the post-cardiac arrest syndrome patient as a marker to capture a brain image of the post-cardiac arrest syndrome patient, and check glucose metabolism therefrom.
  • the image captured by the imaging unit 200 may be transmitted to the information processing unit 100 .
  • the information processing unit 100 may calculate the metabolic function level of the brain based on the captured image.
  • the database unit 300 may store information including the health and nutritional status of a patient who wants to predict the neurological prognosis of post-cardiac arrest syndrome.
  • the database unit 300 includes the information on the metabolic function level of the brain calculated by the information processing unit 100 and a reference value for comparing the calculated SUV (eg, first to fourth values to be described later). can be stored.
  • the system 10 may further include a smartphone 400 - 1 and a computer 400 - 2 as personal terminals.
  • the smartphone 400-1 or the computer 400-2 may transmit and receive data to and from the information processing unit 100 through a communication network, etc., and may include a display unit D or an interface unit (not shown) for this purpose.
  • the method for predicting the neurological prognosis of a patient with post-cardiac arrest syndrome includes: capturing a brain image of a patient with post-cardiac arrest syndrome (S100); It may include calculating the metabolic function level of (S200) and determining the neurological prognosis based on the calculated metabolic function level of the brain (S300).
  • the brain image of the patient with post-cardiac arrest syndrome is captured using the imaging unit 200 .
  • a radiopharmaceutical is injected into the body of a patient with post-cardiac arrest syndrome.
  • the radiopharmaceutical 18F-FDG, a glucose-like substance, may be used, and 18F-FDG may be injected into a patient's body through a vein and absorbed into the brain.
  • a brain image of a patient with post-cardiac arrest syndrome may be captured using the imaging unit 200 . More specifically, the captured image may be an FDG-PET image capable of confirming the degree of absorption of 18F-FDG into the brain of a patient with post-cardiac arrest syndrome.
  • the first period may be 3 hours. That is, in an embodiment of the present invention, an FDG-PET image is captured 3 hours after cardiac arrest occurs, and a neurological prognosis can be predicted therefrom.
  • 3 hours after cardiac arrest occurs preferably between 3 hours and 6 hours, more preferably between 3 hours and Between 4 hours
  • PET images of the patient can be taken, and the neurological prognosis can be predicted early from this.
  • the metabolic function level of the brain of the postcardiac arrest syndrome patient is calculated from the captured image (S200).
  • the information processing unit 100 may receive the captured image from the imaging unit 200 and calculate the metabolic function level of the brain based thereon.
  • the metabolic function level of the brain may indicate the degree to which a radiopharmaceutical (eg, 18F-FDG) injected into the body of a patient with postcardiac arrest syndrome is absorbed into the brain. More specifically, the brain metabolic function level refers to the glucose metabolic function level, which is the degree to which 18F-FDG, a radiopharmaceutical containing glucose, is absorbed into the brain, and may be expressed as SUV.
  • the metabolic function level of the brain of a patient with postcardiac arrest syndrome may be calculated as SUV. More specifically, the information processing unit 100 may calculate the degree of ingestion of 18F-FDG into the brain from the captured image, and based on this, calculate the SUV for each region of the brain and the SUV for the entire brain.
  • the calculating of the metabolic function level of the brain may include an insular cortex, an auditory cortex, a cingulate cortex, a frontal association cortex, and an inner prefrontal cortex among regions of the brain.
  • Medial prefrontal cortex, motor cortex, orbitofrontal cortex, parietal association cortex, retro-splenial cortex, primary somatosensory cortex ), the visual cortex, the hippocampus, the thalamus, the midbrain, the pons, and the medulla, and the SUV of the whole brain can be calculated. .
  • the neurological prognosis of the patient with post-cardiac arrest syndrome is determined (S300).
  • the information processing unit 100 may determine the neurological prognosis of the postcardiac arrest syndrome patient based on the calculated metabolic function level of the brain or the metabolic function level of the brain stored in the database unit 300 .
  • the determining of the neurological prognosis of the patient with post-cardiac arrest syndrome may include determining that the neurological prognosis is bad when the calculated SUV of the brain is lower than the first value.
  • the information processing unit 100 may determine that the neurological prognosis of the postcardiac arrest syndrome patient is bad.
  • the SUV by region of the brain is the insular cortex, auditory cortex, cingulate cortex, prefrontal cortex, medial prefrontal cortex, motor cortex, orbital prefrontal cortex, parietal connective cortex, post-ampullature cortex, primary somatosensory cortex, It may be an SUV of at least one of the visual cortex, hippocampus, thalamus, midbrain, and pons.
  • the first value is a value previously stored in the database unit 300 , and may be a reference value for comparison with the calculated SUV for each region of the brain and SUV for the entire brain.
  • the first value may have different values for each region of the brain and for the entire brain.
  • the information processing unit 100 determines that at least one of the calculated SUVs for each region of the brain is lower than the first value and the calculated SUV of the entire brain is lower than the first value in at least any one of the cases, after cardiac arrest. It can be determined that the neurological prognosis of the syndrome patient is poor.
  • the step of determining the neurological prognosis of the postcardiac arrest syndrome patient may determine that the neurological prognosis is bad when SUV delta, which is the difference between the calculated SUV of the brain and the preset SUV, is higher than the second value. .
  • the information processing unit 100 may determine that the neurological prognosis of the postcardiac arrest syndrome patient is bad.
  • the SUV delta for each brain region is the islet cortex, auditory cortex, cingulate cortex, medial prefrontal cortex, motor cortex, orbital prefrontal cortex, parietal connective cortex, post-amplitude cortex, primary somatosensory cortex, and visual cortex. and at least one SUV delta of the hippocampus.
  • a predetermined SUV may be a value previously stored in the database unit 300, a standard SUV for comparing each area of the SUV delta delta SUV and whole brain of the brain was determined.
  • the reference SUV may be an average value of SUVs for each region of the brain and SUVs for the entire brain in the case where there is no cardiac arrest and no metabolic disease.
  • the preset SUV may have different values for each region of the brain and for the entire brain.
  • the second value is a value previously stored in the database unit 300 and may be a reference value for comparing the calculated SUV for each region of the brain and the difference between the SUV of the entire brain and the reference SUV.
  • the second value may have a different value for each region of the brain and for the entire brain.
  • the information processing unit 100 determines that the difference between at least one of the calculated SUVs for each brain region and the preset SUV is higher than the second value, and the calculated difference between the SUV and the preset SUV for the entire brain is greater than the second value. In at least one of the high cases, it may be determined that the neurological prognosis of the postcardiac arrest syndrome patient is poor.
  • the step of determining the neurological prognosis of the postcardiac arrest syndrome patient includes dividing the calculated SUV for each region of the brain by the SUV of the entire brain, calculating a standardized uptake value ratio (SUVR), and divide the region into a first region and a second region, and in at least any one of a case where the SUVR of the first region is lower than a third value and a case where the SUVR of the second region is higher than a fourth value, the nerve It may include determining that the medical prognosis is bad.
  • SUVR standardized uptake value ratio
  • the information processing unit 100 may calculate SUVR by dividing the calculated SUV for each region of the brain by SUV of the entire brain.
  • the SUV by region of the brain is the cingulate cortex, the prefrontal cortex, the medial prefrontal cortex, the motor cortex, the orbital prefrontal cortex, the parietal connective cortex, the post-ampullar cortex, the primary somatosensory cortex, the visual cortex, the pons, and the medulla oblongata. It may be at least one SUV.
  • the information processing unit 100 divides the brain region into a first region and a second region, and at least among the case where the SUVR of the first region is lower than the third value and the case where the SUVR of the second region is higher than the fourth value In either case, the neurological prognosis may be judged to be poor.
  • the first region is at least any one of the cingulate cortex, the prefrontal cortex, the medial prefrontal cortex, the motor cortex, the orbital prefrontal cortex, the parietal connective cortex, the post-ampullar cortex, the primary somatosensory cortex, and the visual cortex among the brain regions.
  • the cingulate cortex can be one of the prefrontal cortex, the medial prefrontal cortex, the motor cortex, the orbital prefrontal cortex, the parietal connective cortex, the post-ampullar cortex, the primary somatosensory cortex, and the visual cortex among the brain regions.
  • the second region may be at least one of pons and medulla oblongata among regions of the brain.
  • the third value and the fourth value are values previously stored in the database unit 300 , and may be reference values for comparison with the calculated SUVR for each region of the brain.
  • the third value and the fourth value may have different values for each brain region.
  • the third value is for SUVR of the cingulate lobe, prefrontal cortex, medial prefrontal cortex, motor cortex, orbital prefrontal cortex, parietal connective cortex, post-ampullar cortex, primary somatosensory cortex, and visual cortex among brain regions.
  • a reference value, and the fourth value may be a reference value for SUVR of the pons and medulla oblongata.
  • the information processing unit 100 determines that at least one of the SUVR of the first region is lower than the third value and the SUVR of the second region In at least any one of the cases where at least one of the values is higher than the fourth value, it may be determined that the neurological prognosis of the patient with post-cardiac arrest syndrome is poor.
  • the step of determining the neurological prognosis of the postcardiac arrest syndrome patient includes calculating SUVR by dividing the calculated SUV for each area of the brain by SUV of the entire brain, and dividing the brain area into a third area and a fourth area It may include dividing into regions, calculating a ratio of SUVR of the fourth region to SUVR of the third region, and determining that the neurological prognosis is bad when the calculated ratio is lower than a fifth value.
  • the information processing unit 100 may calculate SUVR by dividing the calculated SUV for each region of the brain by SUV of the entire brain.
  • the information processing unit 100 divides the brain region into a third region and a fourth region, calculates a ratio of SUVR of the third region to SUVR of the fourth region, and when the calculated ratio is lower than the fifth value , it can be determined that the neurological prognosis is poor.
  • the third region is a forebrain region, which is the insular cortex, auditory cortex, cingulate lobe, frontal cortex, medial prefrontal cortex, motor cortex, orbital frontal cortex, parietal connective cortex, postamplitude cortex, primary somatosensory cortex and a region including the visual cortex.
  • the fourth region is a hindbrain region, and may be a region including the midbrain, pons, and medulla oblongata.
  • the SUVR ratio of the fourth region to the SUVR of the third region is the ratio of the SUVR of the forebrain to the SUVR of the hindbrain, and can be expressed as a forebrain-to-hindbrain ratio (FHR).
  • FHR forebrain-to-hindbrain ratio
  • the information processing unit 100 may determine that the neurological prognosis is bad.
  • the fifth value is a value previously stored in the database unit 300 and may be a reference value for comparing the calculated ratio of SUVR of the third region to SUVR of the fourth region. In one embodiment, the fifth value may be 1.22.
  • the information processing unit 100 may determine that the neurological prognosis of the postcardiac arrest syndrome patient is poor.
  • test rats pathogen-free male Sprague Dawley rats (18 individuals, 372.5 ⁇ 23.68 g, 12 weeks of age) were used. Experimental rats were bred in a 12-hour day/night cycle and humidity of 50 to 60%, and allowed to freely ingest food and water for 3 weeks before the start of the experiment. For the reporting of results, ARRIVE (Animal Research: Reporting In Vivo Experiments) guidelines were followed.
  • FIG. 3 is a diagram illustrating a model of a method for predicting a neurological prognosis of a patient with post-cardiac arrest syndrome according to an embodiment of the present invention.
  • a PET scan was performed on the subject 2 days before inducing cardiac arrest, thereby obtaining SUVs for each region of the brain.
  • a Morris water maze test was performed on the subject 1 day before the induction of cardiac arrest.
  • PET scan was performed again 3 hours later, and the value of 18F-FDG brain PET as an index of neurological prognosis in the subject with post-cardiac arrest syndrome was investigated.
  • ABGA Arterial blood gas analysis
  • tracheal intubation was performed using an 18 gauge catheter.
  • the subject was mechanically breathed with an oxygen concentration of 21% in volume control mode with a ventilator.
  • Mean arterial pressure (MAP), heart rate, electrocardiography (ECG), and rectal temperature were continuously monitored.
  • MAP Mean arterial pressure
  • ECG electrocardiography
  • rectal temperature was monitored using a temperature control system throughout the experiment and maintained between 36 °C and 37 °C.
  • CPR cardiopulmonary resuscitation
  • the labyrinth used in the experiment consisted of a circular pool (1.83 m in diameter, 0.6 m in depth) filled with water with a black interior maintained at a temperature of 22°C to 24°C.
  • a Plexiglas escape platform (10 cm in diameter) was placed 1 cm below the water surface and invisible to the subject. The platform was placed in one of the quadrants, and the subject was asked to find a hidden platform in the other quadrant. Subjects were trained to make 4 attempts per day for 5 consecutive days, and given 120 seconds per attempt to find a hidden platform.
  • the examination room was located on an exterior wall and contained prominent black and white clues.
  • a computerized tracking system was used to track and record the subject's movement and swimming patterns in the maze. swimming time and distance in the maze were recorded for each trial.
  • subjects were subjected to a Morris water maze test before cardiac arrest and cardiopulmonary resuscitation, and 2 weeks thereafter, respectively.
  • PET scans were performed using a small animal PET system. The spatial resolution at the center of the viewing angle was 1.3 mm. Subjects were allowed to fast for 12 h (12.2 ⁇ 0.9 h) prior to the PET scan. Subjects were general anesthetized with 1.5% isofluorane, and radioactive tracer (9.5 ⁇ 0.7 MBq/0.1 ml) was intravenously injected. One hour after 18F-FDG injection, static brain PET images were obtained for 30 minutes. Subjects were placed on a heating pad in the cage prior to the PET scan, and the temperature was maintained at 30 °C for the duration of the intake. The acquired image was reconstructed into a pixel size of 0.2 mm * 0.2 mm with a thickness of 0.8 mm using the 3D OSEM (3-dimenstional ordered-subset expectation maximization) algorithm.
  • 3D OSEM 3-dimenstional ordered-subset expectation maximization
  • PET data analysis was performed by an experienced nuclear medicine physician using PMOD 3.3 software. PET data obtained over 30 min were manually co-registed into a region of interest (VOI) template for the subject's brain. SUVs were obtained for each volume of interest (VOI). SUV was calculated according to the injected dose and the body weight of the subject. Left and right SUVs were averaged for paired organs. The subregions of the hippocampus and cerebellum were combined.
  • insular cortex auditory cortex, cingulate cortex, frontal association cortex, medial prefrontal cortex, motor cortex, orbital frontal Orbitofrontal cortex, parietal association cortex, retro-splenial cortex, somatosensory cortex, visual cortex, hippocampus, thalamus, midbrain (midbrain), pons, medulla and whole brain VOI were analyzed.
  • the SUV delta of each region was calculated as the difference between SUVs 3 hours after cardiac arrest from the reference SUV (calculated from PET images taken 2 days before cardiac arrest shown in FIG. 3 ).
  • SUV ratio SUV ratio
  • FHR forebrain to hindbrain ratio
  • FIG. 4 is a view showing the results of the Morris underwater maze test according to an embodiment of the present invention.
  • FIG. 4 is a view showing the results of the Morris underwater maze test performed 2 weeks after inducing cardiac arrest to the subject.
  • the circle mark indicates the subject that took less than 50 seconds to pass through the maze, and the triangle mark indicates that the subject passed the maze.
  • This is a test subject that took more than 50 seconds to complete. Subjects that took less than 50 seconds to pass through the maze were classified as having a good neurological prognosis, and subjects that took more than 50 seconds to pass through the maze were classified as poor in neurological prognosis (subjects who died 2 weeks before). also classified as a group with poor neurological prognosis). Blood pressure, heart rate and ABGA were measured before and after the experiment for both groups, and the results were not significantly different between the two groups.
  • Table 1 shows the SUV values for each area of the brain and the SUV values for the whole brain calculated from PET images obtained 3 hours after induction of cardiac arrest (median values in the interquartile range, using the Mann-Whiteny test. *p ⁇ 0.05) .
  • Table 2 shows the difference (SUV delta ) between the reference SUV and the SUV 3 hours after induction of cardiac arrest for each region of the brain and the whole brain (median values in the interquartile range, using the Mann-Whiteny test. *p ⁇ 0.05) .
  • the group with good neurological prognosis had the islet cortex, auditory cortex, cingulate cortex, medial prefrontal cortex, motor cortex, orbital prefrontal cortex, among the brain regions.
  • the SUV delta values were significantly lower in the parietal connective cortex, post-ampullar cortex, primary somatosensory cortex, visual cortex and hippocampus, and the whole brain. That is, in the case of the group with a good neurological prognosis, the difference in SUV values for some areas of the brain and the whole brain was significantly smaller before and after cardiac arrest.
  • Table 3 shows the relative glucose metabolism 3 hours after induction of cardiac arrest. More specifically, SUVR, which is an indicator of relative glucose metabolism, is a value obtained by dividing the SUV value of each brain area by the SUV of the whole brain (median value in the interquartile range, using the Mann-Whiteny test. *p ⁇ 0.05).
  • the SUVR of the posterior cortex, primary somatosensory cortex and visual cortex were significantly higher.
  • the SUVR of the pons and medulla oblongata was significantly lower in the group with good neurological prognosis than the group with poor neurological prognosis.
  • FIG. 5 shows an ROC curve of FHR according to an embodiment of the present invention.
  • FIG. 5 relates to a forebrain to hindbrain ratio (FHR) calculated based on the difference in SUVR of forebrain to hindbrain in PET 3 hours after cardiac arrest according to the prognosis of postcardiac arrest syndrome.
  • the optimal cutoff value was 1.22 (area under the curve 0.969, p ⁇ 0.001), and the FHR predicted an excellent neurological prognosis with a sensitivity of 90% and a specificity of 100%.
  • FIG. 6 is a view showing a PET image and a Morris underwater maze test result according to an embodiment of the present invention.
  • FIG. 6A is a PET image after 3 hours of the group with excellent neurological prognosis
  • FIG. 6B is a PET image after 3 hours of the group with poor neurological prognosis
  • FIG. 6C is the Morris water maze test result of the group with excellent neurological prognosis
  • FIG. 6D is the Morris water maze test result of the group with poor neurological prognosis.
  • the FHR of the group with excellent neurological prognosis was 1.74, and the time and distance required to pass through the maze were 13.1 minutes and 398.2 cm, respectively.
  • the FHR of the group with poor neurological prognosis was 0.85, and the time and distance required to pass through the maze were 114.7 minutes and 2888.9 cm, respectively.
  • the method for predicting the neurological prognosis of a patient with post-cardiac arrest syndrome can predict the neurological prognosis of a patient with post-cardiac arrest syndrome immediately after cardiac arrest occurs.
  • the method for predicting the neurological prognosis of a patient with post-cardiac arrest syndrome evaluates the metabolic function level of the brain from a PET image of a patient with post-cardiac arrest syndrome, and based on the evaluation of the neurological prognosis of a patient with post-cardiac arrest syndrome can be predicted accurately.
  • the embodiment according to the present invention described above may be implemented in the form of a computer program that can be executed through various components on a computer, and such a computer program may be recorded in a computer-readable medium.
  • the medium includes a hard disk, a magnetic medium such as a floppy disk and a magnetic tape, an optical recording medium such as CD-ROM and DVD, a magneto-optical medium such as a floppy disk, and a ROM. , RAM, flash memory, and the like, hardware devices specially configured to store and execute program instructions.
  • the computer program may be specially designed and configured for the present invention, or may be known and available to those skilled in the art of computer software.
  • Examples of the computer program may include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like.
  • connection or connection members of the lines between the components shown in the drawings exemplarily represent functional connections and/or physical or circuit connections, and in an actual device, various functional connections, physical connections that are replaceable or additional may be referred to as connections, or circuit connections.
  • connections, or circuit connections unless there is a specific reference such as "essential”, “importantly”, etc., it may not be a necessary component for the application of the present invention.
  • the present invention can be used in the industry for predicting the neurological prognosis of patients with postcardiac arrest syndrome.

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Abstract

Des modes de réalisation de la présente invention concernent une méthode de prédiction du pronostic neurologique d'un patient atteint d'un syndrome post-arrêt cardiaque. Selon un mode de réalisation de la présente invention, une méthode de prédiction d'un pronostic neurologique d'un patient atteint d'un syndrome post-arrêt cardiaque comprend les étapes consistant : à capturer des images cérébrales d'un patient atteint d'un syndrome post-arrêt cardiaque ; à calculer des niveaux de fonction métabolique du cerveau à partir des images capturées ; et à déterminer un pronostic neurologique sur la base des niveaux de fonction métabolique calculés du cerveau.
PCT/KR2021/005012 2020-04-21 2021-04-21 Méthode de prédiction de pronostic neurologique d'un patient atteint d'un syndrome post-arrêt cardiaque Ceased WO2021215821A1 (fr)

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WO2016079158A1 (fr) * 2014-11-17 2016-05-26 Fundación Centro Nacional De Investigaciones Cardiovasculares Carlos Iii (Cnic) Procédé de prédiction ou de pronostic de performance neurologique chez des patients ayant souffert d'un arrêt cardiaque et éventuellement d'un état comateux dû à la fibrillation ventriculaire.
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KR20190074829A (ko) * 2017-12-20 2019-06-28 서울대학교산학협력단 심정지 환자의 신경학적 예후를 예측하기 위한 바이오마커 및 이의 용도
KR20190132832A (ko) * 2018-05-21 2019-11-29 고려대학교 산학협력단 딥러닝을 기반으로 하는 아밀로이드 양성 또는 음성을 예측하기 위한 방법 및 장치

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