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Reporting and Methods in Developing Prognostic Prediction Models for Metabolic Syndrome: A Systematic Review and Critical Appraisal

Authors Zhang H, Shao J, Chen D, Zou P, Cui N, Tang L, Wang D, Ye Z

Received 25 September 2020

Accepted for publication 19 November 2020

Published 15 December 2020 Volume 2020:13 Pages 4981—4992

DOI https://doi.org/10.2147/DMSO.S283949

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Prof. Dr. Juei-Tang Cheng


Hui Zhang,1 Jing Shao,1 Dandan Chen,1 Ping Zou,2 Nianqi Cui,3 Leiwen Tang,1 Dan Wang,1 Zhihong Ye1

1Department of Nursing, Zhejiang University School of Medicine Sir Run Run Shaw Hospital, Hangzhou, Zhejiang, People’s Republic of China; 2Department of Scholar Practitioner Program, School of Nursing, Nipissing University, Toronto, Ontario, Canada; 3Department of Nursing, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, Zhejiang, People’s Republic of China

Correspondence: Zhihong Ye
Department of Nursing, Zhejiang University School of Medicine Sir Run Run Shaw Hospital, Hangzhou, Zhejiang 310016, People’s Republic of China
Tel +86-13606612119
Email 3192005@zju.edu.cn

Purpose: A prognostic prediction model for metabolic syndrome can calculate the probability of risk of experiencing metabolic syndrome within a specific period for individualized treatment decisions. We aimed to provide a systematic review and critical appraisal on prognostic models for metabolic syndrome.
Materials and Methods: Studies were identified through searching in English databases (PubMed, EMBASE, CINAHL, and Web of Science) and Chinese databases (Sinomed, WANFANG, CNKI, and CQVIP). A checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies (CHARMS) and the prediction model risk of bias assessment tool (PROBAST) were used for the data extraction process and critical appraisal.
Results: From the 29,668 retrieved articles, eleven studies meeting the selection criteria were included in this review. Forty-eight predictors were identified from prognostic prediction models. The c-statistic ranged from 0.67 to 0.95. Critical appraisal has shown that all modeling studies were subject to a high risk of bias in methodological quality mainly driven by outcome and statistical analysis, and six modeling studies were subject to a high risk of bias in applicability.
Conclusion: Future model development and validation studies should adhere to the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) statement to improve methodological quality and applicability, thus increasing the transparency of the reporting of a prediction model study. It is not appropriate to adopt any of the identified models in this study for clinical practice since all models are prone to optimism and overfitting.

Keywords: prediction model, risk, prognosis, metabolic syndrome, systematic review

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