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A model to predict unstable carotid plaques in population with high risk of stroke
Open accessPublished version of a preprint
Abstract
Background: Several models have been developed to predict asymptomatic carotid stenosis (ACS), however these models did not pay much attention to people with lower level of stenosis (<50% or carotid plaques, especially instable carotid plaques) who might benefit from early interventions. Here, we developed a new model to predict unstable carotid plaques through systematic screening in population with high risk of stroke. Methods: Community residents who participated the China National Stroke Screening and Prevention Project (CNSSPP) were screened for their stroke risks. A total of 2841 individuals with high risk of stroke were enrolled in this study, 266 (9.4%) of them were found unstable carotid plaques. A total of 19 risk factors were included in this study. Subjects were randomly distributed into Derivation Set group or Validation Set group. According to their carotid ultrasonography records, subjects in derivation set group were further categorized into unstable plaque group or stable plaque group. Results: 174 cases and 1720 cases from Derivation Set group were categorized into unstable plaque group and stable plaque group respectively. The independent risk factors for carotid unstable plaque were: male (OR 1.966, 95%CI 1.406–2.749), older age (50–59, OR 6.012, 95%CI 1.410–25.629; 60–69, OR 13.915, 95%CI 3.381–57.267;≥70, OR 31.267, 95%CI 7.472–130.83), married(OR 1.780, 95%CI 1.186–2.672), LDL-C(OR 2.015, 95%CI 1.443–2.814), and HDL-C(OR 2.130, 95%CI 1.360–3.338). A predictive scoring system was generated, ranging from 0 to 10. The cut-off value of this predictive scoring system is 6.5. The AUC value for derivation and validation set group were 0.738 and 0.737 respectively. Conclusions: For those individuals with high risk of stroke, we developed a new model which could identify those who have a higher chance to have unstable carotid plaques. When an individual’s predictive model score exceeds 6.5, the probability of having carotid unstable plaques is high, and carotid ultrasonography should be conducted accordingly. This model could be helpful in the primary prevention of stroke.
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Metadata
- Authors
- Yin, J., Yu, C., Liu, H., Du, M., Sun, F., Yu, C., Wei, L., Wang, C., & Wang, X.
- Affiliations
- Nanjing Brain Hospital (CN)
- Nanjing University (CN)
- Corresponding author
- Wang, Xiaoshan
- Venue
- BMC Cardiovascular Disorders
- Published
- 2020-04-07
- Volume
- 20
- Issue
- 1
- Pages
- 164-164
- ISSN
- 1471-2261 (ISSN-L)
- PMC
- PMC7137419
- OpenAlex
- W3014811792
- Cited by
- 10
- References
- 1
- Citations by year
- 2025: 1 · 2024: 1 · 2022: 7 · 2020: 1
- FWCI
- 1.12
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Cardiovascular Disorders (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Medical Science and technology development Foundation, Nanjing Department of Health (YKK18114)
- the General Social Development Medical and Health Project of Nanjing Science and Technology Commission (201803029)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cerebrovascular and Carotid Artery Diseases
- Health Sciences › Medicine › Clinical Neurology › Acute Ischemic Stroke Management
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Disease Prevention
- Keywords
- unstable carotid plaque, carotid artery stenosis, stroke risk prediction, predictive scoring system, carotid ultrasound, stroke screening, risk factors, LDL cholesterol, HDL cholesterol, sex differences, age, early intervention, community-based screening, risk prediction model validation, risk prediction model
- MeSH terms
- Clinical Decision Rules, Adult, Aged, China, Female, Humans, Male, Middle Aged, Predictive Value of Tests, Prognosis, Risk Factors, Rupture, Spontaneous, Prevalence, Databases, Factual, Carotid Stenosis, Risk Assessment, Stroke, Plaque, Atherosclerotic
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