ArticleFrontiers in cardiovascular medicine2022
Development and validation of a carotid atherosclerosis risk prediction model based on a Chinese population.
Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 17 citations in OpenAlex.
- Development and Internal Validation of a Risk Prediction Model for Carotid Atherosclerosis in the Hyperuricemia Population.Vascular health and risk management · 2024Trial
- The association between plasma IgG N-glycosylation and viral encephalitis in children: a hospital-based case-control study.Italian journal of pediatrics · 2026Article
- Development and external validation of a machine learning model for cardiac valve calcification early screening in dialysis patients: a multicenter study.Renal failure · 2025Article
- Interpretable machine learning model for predicting MII oocyte retrieved following controlled ovarian stimulation: a retrospective cohort study of 24,976 IVF/ICSI cycles.Journal of assisted reproduction and genetics · 2025Article
- Immunoglobulin G N-glycosylation predicts long-term risk of ischemic stroke: a nested case-control study.Journal of translational medicine · 2025Article
- A novel nomogram for predicting carotid atherosclerosis risks in diabetic patients.BMC cardiovascular disorders · 2025Article
- Development and Validation of a Nomogram for Predicting Carotid Atherosclerosis in Non-Obese Patients with Type 2 Diabetes.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Article
- Association between metabolic score of visceral fat and carotid atherosclerosis in Chinese health screening population: a cross-sectional study.BMC public health · 2024Article
- Screening for carotid atherosclerosis: development and validation of a high-precision risk scoring tool.Frontiers in cardiovascular medicine · 2024Article
- Machine learning approaches for biomarker discovery to predict large-artery atherosclerosis.Scientific reports · 2023Article
- Predicting the 5-Year Risk of Nonalcoholic Fatty Liver Disease Using Machine Learning Models: Prospective Cohort Study.Journal of medical Internet research · 2023Article
- Development and validation of a nomogram for evaluating the incident risk of carotid atherosclerosis in patients with type 2 diabetes.Frontiers in endocrinology · 2023Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: This study aimed to identify independent risk factors for carotid atherosclerosis (CAS) and construct and validate a CAS risk prediction model based on the Chinese population. Methods: This retrospective study included 4,570 Chinese adults who underwent health checkups (including carotid ultrasound) at the Zhenhai Lianhua Hospital, Ningbo, China, in 2020. All the participants were randomly assigned to the training and validation sets at a ratio of 7:3. Independent risk factors associated with CAS were identified using multivariate logistic regression analysis. The least absolute shrinkage and selection operator combined with 10-fold cross-validation were screened for characteristic variables, and nomograms were plotted to demonstrate the risk prediction model. C-index and receiver operating characteristic curves, calibration plots, and decision curve analysis (DCA) were used to evaluate the risk model's discrimination, calibration, and clinical applicability. Results: Age, body mass index, diastolic blood pressure, white blood cell count, mean platelet volume, alanine transaminase, aspartate transaminase, and gamma-glutamyl transferase were identified as independent risk factors for CAS. In the training, internal validation, and external validation sets, the risk model showed good discriminatory power with C-indices of 0.961 (0.953-0.969), 0.953 (0.939-0.967), and 0.930 (0.920-0.940), respectively, and excellent calibration. The results of DCA showed that the prediction model could be beneficial when the risk threshold probabilities were 1-100% in all sets. Finally, a network computer (dynamic nomogram) was developed to facilitate the physicians' clinical operations. The website is https://nbuhgq.shinyapps.io/DynNomapp/. Conclusion: The development of risk models contributes to the early identification and prevention of CAS, which is important for preventing and reducing adverse cardiovascular and cerebrovascular events.
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