Prediction model for major adverse cardiovascular events based on thoracic artery calcification and construction method

A technology of prediction model and construction method, applied in cardiac catheterization, instruments for radiological diagnosis, medical simulation, etc., can solve the problem of loss of calcification information, achieve high specificity and sensitivity, improve prediction performance, and good MACEs prediction ability Effect

Pending Publication Date: 2021-10-08
THE EIGHTH AFFILIATED HOSPITAL SUN YAT SEN UNIV
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Problems solved by technology

However, judging from the early calcification information found by 18F-NaF PET / CT and the fact that the calcification is essentially a heterogeneous lesion, part of the calcification information must be lost in the evaluation of calcification by the Agatston score

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  • Prediction model for major adverse cardiovascular events based on thoracic artery calcification and construction method
  • Prediction model for major adverse cardiovascular events based on thoracic artery calcification and construction method
  • Prediction model for major adverse cardiovascular events based on thoracic artery calcification and construction method

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Embodiment

[0076] It should be noted that in this embodiment, all data statistics of the present invention are analyzed under SPSS 25 or R 3.6.3 software. Since the continuous variables involved in the present invention do not obey the normal distribution, the median (interquartile interval) is used to represent, and Mann-Whitney U test is used to compare between two groups. Categorical variables were expressed as numerical values ​​(percentages), and comparisons between two groups were performed using the x2 test or Fisher's exact probability method. Cox regression analysis was used to screen model features and construct predictive models. The "glmnet" package was used to run LASSO regression analyses. The "pROC" package was used to plot ROC curves. The "rms" package was used to plot nomograms and calibration curves. "code source stdca.R" is used to draw decision analysis curves. Two-sided p<0.05 was considered to be statistically different.

[0077] 1. Patient screening and data c...

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Abstract

The invention discloses a prediction model for major adverse cardiovascular events based on thoracic artery calcification and a construction method. The method adopts computer high-throughput image features, enriches the description of calcification features, and thus improves the prediction accuracy of calcification indexes for MACEs. The image omics features of thoracic artery calcification are extracted based on CTACS by using an image omics analysis method, and new parameters for predicting MACEs, namely image omics integrals, are constructed. The parameters are significantly superior to traditional calcification evaluation parameters such as CTACS and CACS in predicting MACEs. At the same time, an image omics-clinical variable prediction model is constructed based on image omics integrals. The model has good prediction performance, can accurately predict whether MACEs occur in the future for patients, and assists doctors in individualized cardiovascular prevention and treatment, treatment schemes are adjusted timely, insufficient or excessive treatment is avoided, prognosis of patients is improved, life quality is also improved, and clinical application value is high.

Description

technical field [0001] The invention belongs to the field of biomedicine, and in particular relates to a prediction model and a construction method for major adverse cardiovascular events based on thoracic artery calcification. [0002] technical background [0003] In the past 30 years, cardiovascular disease has been the leading cause of death for all human beings, and China is the main hardest hit area. Cardiovascular burden is still the most urgent health problem to be solved in China. Major adverse cardiovascular events (Majoradverse cardiovascular events, MACEs) are closely related to the death and prognosis of cardiovascular diseases, and the occurrence of MACEs increases the cardiovascular burden. Therefore, establishing an accurate MACEs prediction model is one of the important measures to reduce the cardiovascular burden. [0004] At present, relevant MACEs prediction models are established based on traditional cardiovascular risk factors; however, with the deepeni...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B6/03A61B6/00G16H30/00G16H50/50
CPCA61B6/032A61B6/503A61B6/504A61B6/5211G16H50/50G16H30/00
Inventor 黄辉朱永俊王秋雨韩峰马祥园陈洁
Owner THE EIGHTH AFFILIATED HOSPITAL SUN YAT SEN UNIV
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