Journal
DIABETES METABOLIC SYNDROME AND OBESITY-TARGETS AND THERAPY
Volume 14, Issue -, Pages 3027-3034Publisher
DOVE MEDICAL PRESS LTD
DOI: 10.2147/DMSO.S316950
Keywords
prediction model; prognosis; metabolic syndrome; algorithms; calibration; discrimination
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Funding
- Zhejiang province medical technology project [WKJ-ZJ-1925]
- National Social Science Fund of China [20BGL275]
- Postdoctoral Foundation of Zhejiang Province [zj2019022]
- National Natural Science Foundation of China [72004193]
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The externally validated prediction model for metabolic syndrome risk in adults showed good discrimination and calibration, indicating its accuracy in predicting the risk. However, further validation in international and prospective cohorts is recommended for broader applicability.
Purpose: A 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 Methods: A 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. Results: In 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. Conclusion: The 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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