ArticleDiabetes, metabolic syndrome and obesity : targets and therapy2021
External Validation of the Prognostic Prediction 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 8 papers.
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Who cites it
8 citing papers in PubMed.
- Risk Factors for Cardiovascular Disease: Epidemiology, Screening, Prevention, and Therapeutic Interventions.MedComm · 2026Review
- Development and Validation of the Predicting Risk of Ischemic Stroke in Malignancy Estimation Tool.Journal of the American Heart Association · 2026Article
- Construction and temporal external validation of interpretable machine-learning models for predicting tigecycline-associated hypofibrinogenemia.European journal of clinical pharmacology · 2026Article
- Article
- 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
- 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
- Article
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Authors and funding
9 authors.
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Abstract
purposeA prediction model for 4-year risk of metabolic syndrome in adults was previously developed and internally validated. However, external validity or generalizability for this model was not assessed so it is not appropriate for clinical application. We aimed to externally validate this model based on a retrospective cohort. PATIENTS AND
methodsA retrospective cohort design and a temporal validation strategy were used in this study based on a dataset from 1 January 2015 to 31 December 2018. Multiple imputation was used for missing values. Model performance was evaluated by using discrimination, calibration (calibration plot, calibration slope, and calibration intercept), overall performance (Brier score), and decision curve analysis.
resultsIn external validation, the C-statistic was 0.782 (95% CI, 0.771-0.793). The calibration plot shows good calibration, calibration slope was 1.006 (95% CI, -0.011-1.063), and calibration intercept was -0.045 (95% CI, -0.113-0.022). Brier score was 0.164.The discrimination and calibration of the prediction model were good in temporal external validation.
conclusionThe discrimination and calibration of the prediction model were satisfactory in the temporal external validation. However, clinicians should be aware that this prediction model was developed and validated in a tertiary setting. It is strongly recommended that further studies validate this model in international cohorts and large, prospective cohorts in different institutions.
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