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.