ArticleDiabetes, metabolic syndrome and obesity : targets and therapy2021
Development and Internal Validation of a Prognostic Model for 4-Year Risk of Metabolic Syndrome in Adults: A Retrospective Cohort Study.
Article in Diabetes, metabolic syndrome and obesity : targets and therapy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Risk prediction models for dental caries in children and adolescents: a systematic review and meta-analysis.BMJ open · 2025Pooled it
- Risk-prediction models for intravenous immunoglobulin resistance in Kawasaki disease: Risk-of-Bias Assessment using PROBAST.Pediatric research · 2023Article
- The integrated common-sense model of illness self-regulation: predicting healthy eating, exercise behaviors, and health among individuals at risk of metabolic syndrome.BMC public health · 2023Article
- Development of predictive equation and score for 5-year metabolic syndrome incidence in Japanese adults.PloS one · 2023Article
- Development and visualization of a risk prediction model for metabolic syndrome: a longitudinal cohort study based on health check-up data in China.Frontiers in nutrition · 2023Article
- Using noninvasive anthropometric indices to develop and validate a predictive model for metabolic syndrome in Chinese adults: a nationwide study.BMC endocrine disorders · 2022Article
- Design and development of a Wechat applet for intelligent health management of metabolic syndrome.Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences · 2022Article
- Machine Learning-Based Prediction for 4-Year Risk of Metabolic Syndrome in Adults: A Retrospective Cohort Study.Risk management and healthcare policy · 2021Article
- External Validation of the Prognostic Prediction Model for 4-Year Risk of Metabolic Syndrome in Adults: A Retrospective Cohort Study.Diabetes, metabolic syndrome and obesity : targets and therapy · 2021Article
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8 authors.
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Abstract
purposeA prognostic prediction model for metabolic syndrome can help nurses or physicians evaluate the future individual absolute risk of MetS in order to develop personalized care strategies. We aimed to derive and internally validate a prognostic prediction model for 4-year risk of metabolic syndrome in adults. PATIENTS AND
methodsThis was a retrospective cohort study conducted in a tertiary care setting, and the dataset was obtained from the Healthcare Information and Management Systems of a tertiary hospital. The cohort included Chinese adults attending health examination from 1 January 2011 to 31 December 2014. A total of 6793 participants without metabolic syndrome were included in the cohort and were followed up for 4 years. Available candidate predictors in the dataset were weight, MCV, MCH, AST, ALT, BMI, NGC, TC, serum uric acid, gender, smoking, WBC, LC, Hb, HCT, and age. A logistic regression model was adopted to build the risk equation, and bootstrapping was used when considering internal validation. Calibration, discrimination, and the clinical utility were calculated for the model's performance.
resultsOf the 6793 participants, 1750 participants were diagnosed with metabolic syndrome within 4 years. The developed prediction model contained 5 predictors (body mass index, age, total cholesterol, alanine transaminase, and serum uric acid). After internal validation, the C-statistic was 0.783 (95% CI, 0.772-0.795). Additionally, the current model had good calibration. Calibration slope was 0.995 (95% CI, 0.934-1.058), and calibration intercept was -0.008 (95% CI, -0.088-0.073). The Brier score was 0.156. The decision-curve analysis indicated that the prediction model provided greater net benefit than the default strategies of providing treatment or not providing treatment for all patients.
conclusionA prognostic risk prediction model for determining 4-year risk of metabolic syndrome onset in adults was developed and internally validated. This model was based on routine clinical measurements that quantified individual future risk of metabolic syndrome.
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