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Nomogram Based on Cytokines for Cardiovascular Diseases in Xinjiang Kazakhs
BACKGROUND: This study involved the development of a predictive 5-year morbidity nomogram for cardiovascular diseases (CVD) in Xinjiang Kazakhs based on cytokine levels. METHODS: The nomogram was based on a baseline survey of the town of Nalati in the Kazakh Autonomous Prefecture of Xinjiang from 20...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6525937/ https://www.ncbi.nlm.nih.gov/pubmed/31191115 http://dx.doi.org/10.1155/2019/4756295 |
Sumario: | BACKGROUND: This study involved the development of a predictive 5-year morbidity nomogram for cardiovascular diseases (CVD) in Xinjiang Kazakhs based on cytokine levels. METHODS: The nomogram was based on a baseline survey of the town of Nalati in the Kazakh Autonomous Prefecture of Xinjiang from 2009 to 2013. By 2016, we had monitored 1508 people for a median time of 5.17 years and identified CVD events in the study population by collecting case information from local hospitals. The study population was divided into the training (n = 1005) and validation cohorts (n = 503) in a 2 : 1 ratio. The area under the receiver operating characteristic curve (AUC) was used to verify the predictive accuracy of the nomogram. The result was assessed in a validation cohort. RESULTS: At the end of the study, the incidence of CVD in Xinjiang Kazakhs was found to be 11.28%. We developed a new nomogram to predict the 5-year incidence of CVD based on age, interleukin-6 (IL-6), and adiponectin (APN) levels, diastolic blood pressure, and dyslipidemia. The AUC for the predictive accuracy of the nomogram was 0.836 (95% confidence interval: 0.802–0.869), which was higher than that for IL-6 and APN. These results were supported by validation studies. CONCLUSIONS: The nomogram model can more directly assess the risk of CVD in Kazakhs and can be used for CVD risk assessment. |
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