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Du X, Chen F, He Y, Zou H, Pan H, Zhu X. Establishment and validation of prediction model for atherosclerotic cardiovascular disease in patients with hyperuricemia. Int J Rheum Dis 2024; 27:e15205. [PMID: 38873791 DOI: 10.1111/1756-185x.15205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 05/10/2024] [Accepted: 05/13/2024] [Indexed: 06/15/2024]
Abstract
OBJECTIVE To construct a risk prediction model for atherosclerotic cardiovascular disease (ASCVD) in patients with hyperuricemia. METHODS Data in this study were obtained from the National Health and Nutrition Examination Survey (NHANES) (2007-2010). Participants from Huashan Hospital were included as an external validation. Logistic regression analysis was used to explore the relevant factors of ASCVD in patients with hyperuricemia. The discriminability of the model was evaluated using the area under the curve (AUC) statistic of the receiver operating characteristic curve. Hosmer-Lemeshow test, correction curve and decision curve analysis (DCA) were used to evaluate the model. RESULTS A total of 389 patients collected from the NHANES were included in the final analysis. Logistic regression analysis showed that age, creatinine (Cr), glucose (Glu), serum uric acid (SUA), and history of gout were predictive factors for ASCVD in hyperuricemia (HUA) patients. These predictive factors were used to construct a nomogram. And 157 patients from NHANES were in the internal validation group and 136 patients from Huashan Hospital were in the external validation group. The AUC values of the three groups were 0.943, 0.735, and 0.664. The p values of the Hosmer-Lemeshow test were .568, .600, and .763. The calibration curve showed consistency between the nomogram and the actual observed values. The DCA curve indicated that the model has good clinical practicality. CONCLUSION This study constructed the ASCVD risk prediction model for HUA patients, which is beneficial for medical staff to detect high-risk populations of ASCVD in the early stage.
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Affiliation(s)
- Xingchen Du
- Department of Rheumatology, Huashan Hospital, Fudan University, Shanghai, China
| | - Fangfang Chen
- Department of Rheumatology, Huashan Hospital, Fudan University, Shanghai, China
| | - Yisheng He
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - Hejian Zou
- Department of Rheumatology, Huashan Hospital, Fudan University, Shanghai, China
| | - HaiFeng Pan
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - Xiaoxia Zhu
- Department of Rheumatology, Huashan Hospital, Fudan University, Shanghai, China
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Fu J, Deng Y, Ma Y, Man S, Yang X, Yu C, Lv J, Wang B, Li L. National and Provincial-Level Prevalence and Risk Factors of Carotid Atherosclerosis in Chinese Adults. JAMA Netw Open 2024; 7:e2351225. [PMID: 38206625 PMCID: PMC10784858 DOI: 10.1001/jamanetworkopen.2023.51225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 11/21/2023] [Indexed: 01/12/2024] Open
Abstract
Importance Epidemiologic studies on carotid atherosclerosis (CAS) based on nationwide ultrasonography measurements can contribute to understanding the future risk of cardiovascular diseases and identifying high-risk populations, thereby proposing more targeted prevention and treatment measures. Objectives To estimate the prevalence of CAS within the general population of China and to investigate its distribution among populations with potential risk factors and variation across diverse geographic regions. Design, Setting, and Participants This multicenter, population-based cross-sectional study used China's largest health check-up chain database to study 10 733 975 individuals aged 20 years or older from all 31 provinces in China who underwent check-ups from January 1, 2017, to June 30, 2022. Main Outcomes and Measures Carotid atherosclerosis was assessed and graded using ultrasonography as increased carotid intima-media thickness (cIMT), carotid plaque (CP), and carotid stenosis (CS). The overall and stratified prevalences were estimated among the general population and various subpopulations based on demographic characteristics, geographic regions, and cardiovascular disease risk factors. Mixed-effects regression models were used to analyze the risk factors for CAS. Results Among 10 733 975 Chinese participants (mean [SD] age, 47.7 [13.4] years; 5 861 566 [54.6%] male), the estimated prevalences were 26.2% (95% CI, 25.0%-27.4%) for increased cIMT, 21.0% (95% CI, 19.8%-22.2%) for CP, and 0.56% (95% CI, 0.36%-0.76%) for CS. The prevalence of all CAS grades was higher among older adults (eg, increased cIMT: aged ≥80 years, 92.7%; 95% CI, 92.2%-93.3%), male participants (29.6%; 95% CI, 28.4%-30.7%), those residing in northern China (31.0%; 95% CI, 29.1%-32.9%), and those who had comorbid conditions, such as hypertension (50.8%; 95% CI, 49.7%-51.9%), diabetes (59.0%; 95% CI, 57.8%-60.1%), dyslipidemia (32.1%; 95% CI, 30.8%-33.3%), and metabolic syndrome (31.0%; 95% CI, 29.1%-32.9%). Most cardiovascular disease risk factors were independent risk factors for all CAS stages (eg, hypertension: 1.60 [95% CI, 1.60-1.61] for increased cIMT, 1.62 [95% CI, 1.62-1.63] for CP, and 1.48 [95% CI, 1.45-1.51] for CS). Moreover, the magnitude of the association between several cardiovascular disease risk factors and increased cIMT and CP differed between the sexes and geographic regions. Conclusions and Relevance These findings suggest that nearly one-quarter of Chinese adults have increased cIMT or CP. The burden of this disease is unevenly distributed across geographic regions and subpopulations and may require different levels of local planning, support, and management. Addressing these disparities is crucial for effectively preventing and managing cardiovascular and cerebrovascular diseases in China.
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Affiliation(s)
- Jingzhu Fu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Meinian Public Health Institute, Peking University Health Science Center, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Yuhan Deng
- Meinian Institute of Health, Beijing, China
- Department of Social Medicine and Health Education, School of Public Health, Peking University, Beijing, China
- Chongqing Research Institute of Big Data, Peking University, Chongqing, China
| | - Yuan Ma
- Meinian Institute of Health, Beijing, China
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Sailimai Man
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Meinian Public Health Institute, Peking University Health Science Center, Beijing, China
- Meinian Institute of Health, Beijing, China
| | - Xiaochen Yang
- Meinian Institute of Health, Beijing, China
- Department of Social Medicine and Health Education, School of Public Health, Peking University, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Meinian Public Health Institute, Peking University Health Science Center, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Meinian Public Health Institute, Peking University Health Science Center, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Bo Wang
- Meinian Public Health Institute, Peking University Health Science Center, Beijing, China
- Meinian Institute of Health, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Meinian Public Health Institute, Peking University Health Science Center, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
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