Zhang Hui, Shao Jing, Chen Dandan, Zou Ping, Cui Nianqi, Tang Leiwen, Wang Dan, Ye Zhihong
Department of Nursing, Zhejiang University School of Medicine Sir Run Run Shaw Hospital, Hangzhou, Zhejiang, People's Republic of China.
Department of Scholar Practitioner Program, School of Nursing, Nipissing University, Toronto, Ontario, Canada.
Diabetes Metab Syndr Obes. 2020 Dec 15;13:4981-4992. doi: 10.2147/DMSO.S283949. eCollection 2020.
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.
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.
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.
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.
代谢综合征的预后预测模型可以计算出在特定时间段内发生代谢综合征风险的概率，以便做出个性化的治疗决策。我们旨在对代谢综合征的预后模型进行系统评价和批判性评估。
通过检索英文数据库（PubMed、EMBASE、CINAHL和Web of Science）和中文数据库（中国生物医学文献数据库、万方、知网和维普）来识别研究。在数据提取过程和批判性评估中，使用了预测建模研究系统评价的批判性评估和数据提取清单（CHARMS）以及预测模型偏倚风险评估工具（PROBAST）。
从检索到的29668篇文章中，本综述纳入了11项符合选择标准的研究。从预后预测模型中识别出48个预测因子。c统计量范围为0.67至0.95。批判性评估表明，所有建模研究在方法学质量上都存在较高的偏倚风险，主要由结果和统计分析驱动，并且六项建模研究在适用性方面存在较高的偏倚风险。
未来的模型开发和验证研究应遵循个体预后或诊断多变量预测模型透明报告（TRIPOD）声明，以提高方法学质量和适用性，从而增加预测模型研究报告的透明度。由于所有模型都容易出现乐观估计和过度拟合的情况，因此在本研究中识别出的任何模型都不适用于临床实践。
