External Validation of the Prognostic Prediction Model for 4-Year Risk of Metabolic Syndrome in Adults: A Retrospective Cohort Study

Zhang, Hui; Chen, Dandan; Jin, Shao; Zou, Ping; Cui, Nianqi; Tang, Leiwen; Wang, Xiyi; Wang, Dan; Ye, Zhihong

doi:10.2147/dmso.s316950

Cited by 7 publications

(10 citation statements)

References 21 publications

(26 reference statements)

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“…Based on our previous work, age (years), total cholesterol (TC, mmol/l), serum uric acid (UA, μmol/l), alanine transaminase (ALT, U/L), and body mass index (BMI, Kg/m 2 ) were identified as predictors in the prognostic prediction model. 9 , 10 Therefore, these specified predictors were included in ML-based models.…”

Section: Methodsmentioning

confidence: 99%

“…The ML-based prediction model employed the same inclusion criteria, candidate predictors, and outcome definition, which were used in the logistic regression-based prediction model. 9 , 10 Optimal hyperparameters were selected to enable the ML algorithms to work optimally ( Supplement file 1 ).…”

Section: Methodsmentioning

confidence: 99%

“…Risk Management and Healthcare Policy 2021:14 model. 9,10 Therefore, these specified predictors were included in ML-based models.…”

Section: Dovepressmentioning

confidence: 99%

“…Therefore, we developed a prediction model for 4-year risk of metabolic syndrome in adults using logistic regression which was internally and externally validated based on health examination cohorts. 9 , 10 The reproducibility and generalizability of this prognostic model was also determined.…”

Section: Introductionmentioning

confidence: 99%

“…Herein, we present ML-based methods for the prediction of 4-year risk of metabolic syndrome in adults and compare ML model performance with the previous model using logistic regression. 9 , 10 …”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Based Prediction for 4-Year Risk of Metabolic Syndrome in Adults: A Retrospective Cohort Study

Zhang¹,

Chen²,

Zou³

et al. 2021

RMHP

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Machine learning (ML) techniques have emerged as a promising tool to predict risk and make decisions in different medical domains. We aimed to compare the predictive performance of machine learning-based methods for 4-year risk of metabolic syndrome in adults with the previous model using logistic regression. Patients and Methods: This was a retrospective cohort study that employed a temporal validation strategy. Three popular ML techniques were selected to build the prognostic models. These techniques were artificial neural networks, classification and regression tree, and support vector machine. The logistic regression algorithm and ML techniques used the same five predictors. Discrimination, calibration, Brier score, and decision curve analysis were compared for model performance. Results: Discrimination was above 0.7 for all models except classification and regression tree model in internal validation, while the logistic regression model showed the highest discrimination in external validation (0.782) and the smallest discrimination differences. The logistic regression model had the best calibration performance, and ANN also showed satisfactory calibration in internal validation and external validation. For overall performance, logistic regression had the smallest Brier score differences in internal validation and external validation, and it also had the largest net benefit in external validation. Conclusion: Overall, this study indicated that the logistic regression model performed as well as the flexible ML-based prediction models at internal validation, while the logistic regression model had the best performance at external validation. For clinical use, when the performance of the logistic regression model is similar to ML-based prediction models, the simplest and more interpretable model should be chosen.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…Risk Management and Healthcare Policy 2021:14 model. 9,10 Therefore, these specified predictors were included in ML-based models.…”

Section: Dovepressmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Based Prediction for 4-Year Risk of Metabolic Syndrome in Adults: A Retrospective Cohort Study

Zhang¹,

Chen²,

Zou³

et al. 2021

RMHP

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Development and validation of a preliminary clinical support system for measuring the probability of incident 2-year (pre)frailty among community-dwelling older adults: A prospective cohort study

Liu

Yang

Shi

et al. 2023

International Journal of Medical Informatics

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Development and validation of a risk scoring tool for predicting incident reversible cognitive frailty among community‐dwelling older adults: A prospective cohort study

Liu,

Si,

et al. 2024

Geriatrics Gerontology Int

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AimReversible cognitive frailty (RCF) is an ideal target to prevent asymptomatic cognitive impairment and dependency. This study aimed to develop and validate prediction models for incident RCF.MethodsA total of 1230 older adults aged ≥60 years from China Health and Retirement Longitudinal Study 2011–2013 survey were included as the training set. The modified Poisson regression and three machine learning algorithms including eXtreme Gradient Boosting, support vector machine and random forest were used to develop prediction models. All models were evaluated internally with fivefold cross‐validation, and evaluated externally using a temporal validation method through the China Health and Retirement Longitudinal Study 2013–2015 survey.ResultsThe incidence of RCF was 27.4% in the training set and 27.5% in the external validation set. A total of 13 important predictors were selected to develop the model, including age, education, contact with their children, medical insurance, vision impairment, heart diseases, medication types, self‐rated health, pain locations, loneliness, self‐medication, night‐time sleep and having running water. All models showed acceptable or approximately acceptable discrimination (AUC 0.683–0.809) for the training set, but fair discrimination (AUC 0.568–0.666) for the internal and external validation. For calibration, only modified Poisson regression and eXtreme Gradient Boosting were acceptable in the training set. All models had acceptable overall prediction performance and clinical usefulness. Older adults were divided into three groups by the risk scoring tool constructed based on modified Poisson regression: low risk (≤24), median risk (24–29) and high risk (>29).ConclusionsThis risk tool could assist healthcare providers to predict incident RCF among older adults in the next 2 years, facilitating early identification of a high‐risk population of RCF. Geriatr Gerontol Int 2024; ••: ••–••.

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External Validation of the Prognostic Prediction Model for 4-Year Risk of Metabolic Syndrome in Adults: A Retrospective Cohort Study

Cited by 7 publications

References 21 publications

Machine Learning-Based Prediction for 4-Year Risk of Metabolic Syndrome in Adults: A Retrospective Cohort Study

Machine Learning-Based Prediction for 4-Year Risk of Metabolic Syndrome in Adults: A Retrospective Cohort Study

Development and validation of a preliminary clinical support system for measuring the probability of incident 2-year (pre)frailty among community-dwelling older adults: A prospective cohort study

Development and validation of a risk scoring tool for predicting incident reversible cognitive frailty among community‐dwelling older adults: A prospective cohort study

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