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Method for establishing carotid arteriosclerosis and carotid plaque prediction model for common people

A technology for predicting models and arteriosclerosis, applied in the medical field, can solve problems such as model calibration evaluation, and achieve the effect of ensuring robustness, high prediction efficiency, and improving prediction performance

Pending Publication Date: 2020-06-09
ZHEJIANG UNIV
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Problems solved by technology

Although the AUC of the prediction model established by the above studies is high, there is still room for improvement, and none of the prediction models reported in previous studies evaluated the calibration of the model (the previous studies did not pay attention to this evaluation index, but more Focus on discrimination AUC

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  • Method for establishing carotid arteriosclerosis and carotid plaque prediction model for common people
  • Method for establishing carotid arteriosclerosis and carotid plaque prediction model for common people
  • Method for establishing carotid arteriosclerosis and carotid plaque prediction model for common people

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specific Embodiment

[0048] 1) Selection of modeling population: Select individuals (8308 cases) who underwent physical examination in Hangzhou Wuyunshan Sanatorium from March 2016 to December 2018 as the research objects. Exclusion criteria: ① those with incomplete information entry; ② malignant tumors; ④ connective tissue system diseases; ⑤ blood system diseases; ⑥ autoimmune system diseases; ⑦ osteoporosis; ⑧ severe liver and kidney damage; ⑨ mental disorders.

[0049] 2) Data collection: including gender, age, past medical history, etc. (2) Physiological measurement data: including height, weight, blood pressure and other items. (3) Laboratory biochemical test data: including alkaline phosphatase, alanine aminotransferase, γ-glutamyl transpeptidase (GGT), total bilirubin, direct bilirubin, indirect bilirubin, fasting Blood sugar, serum total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides and uric acid. (4) Carotid artery ...

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Abstract

The invention relates to the technical field of medicine, and discloses a method for establishing a carotid arteriosclerosis and carotid plaque prediction model for common people. The method comprisesthe following steps: 1) selecting common people as modeling people; 2) collecting information; 3) grouping the modeling people, concretely, dividing the modeling people into a normal group and an abnormal group by taking the IMT maximum value of carotid arteries at the two sides as an evaluation index; 4) statistical modeling; and 5) prediction model evaluation. The method has the advantages thatthe prediction model is suitable for common physical examination crowds rather than high-risk crowds, the prediction efficiency of the prediction model is high, AUC reaches 0.88 or above, and the accuracy of the prediction model is 80% or above.

Description

technical field [0001] The invention relates to the field of medical technology, in particular to a method for establishing a carotid atherosclerosis and carotid plaque prediction model for ordinary people. Background technique [0002] Carotid atherosclerosis and carotid plaque formation are common vascular wall lesions, manifested as thickening, sclerosis, and remodeling of the vascular wall, which can significantly increase the risk of myocardial infarction, coronary heart disease, and ischemic stroke. The study found that with every 0.1mm increase in carotid intima-media thickness, the risk of stroke and myocardial infarction both increased by more than 10%. In the past, it was found that advanced age, male sex, obesity, smoking, high blood pressure, too little physical activity, and high and low-density lipoproteinemia were risk factors for carotid plaque and carotid atherosclerosis. At present, there are few domestic reports on the prediction model of carotid artery p...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/20G16H50/30
CPCG16H50/20G16H50/30
Inventor 余运贤邵布勒莫敏佳
Owner ZHEJIANG UNIV
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