Division of Biomedical Sciences, University of Warwick, Coventry, UK.
University Hospitals Coventry and Warwickshire, Coventry, UK.
BJOG. 2024 Nov;131(12):1591-1602. doi: 10.1111/1471-0528.17802. Epub 2024 Mar 11.
The identification of large for gestational age (LGA) and macrosomic fetuses is essential for counselling and managing these pregnancies.
To systematically review the literature for multivariable prediction models for LGA and macrosomia, assessing the performance, quality and applicability of the included model in clinical practice.
MEDLINE, EMBASE and Cochrane Library were searched until June 2022.
We included observational and experimental studies reporting the development and/or validation of any multivariable prediction model for fetal macrosomia and/or LGA. We excluded studies that used a single variable or did not evaluate model performance.
Data were extracted using the Checklist for critical appraisal and data extraction for systematic reviews of prediction modelling studies checklist. The model performance measures discrimination, calibration and validation were extracted. The quality and completion of reporting within each study was assessed by its adherence to the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) checklist. The risk of bias and applicability were measured using PROBAST (Prediction model Risk Of Bias Assessment Tool).
A total of 8442 citations were identified, with 58 included in the analysis: 32/58 (55.2%) developed, 21/58 (36.2%) developed and internally validated and 2/58 (3.4%) developed and externally validated a model. Only three studies externally validated pre-existing models. Macrosomia and LGA were differentially defined by many studies. In total, 111 multivariable prediction models were developed using 112 different variables. Model discrimination was wide ranging area under the receiver operating characteristics curve (AUROC 0.56-0.96) and few studies reported calibration (11/58, 19.0%). Only 5/58 (8.6%) studies had a low risk of bias.
There are currently no multivariable prediction models for macrosomia/LGA that are ready for clinical implementation.
识别巨大儿（LGA）和胎儿巨大儿对于咨询和管理这些妊娠至关重要。
系统回顾文献中用于 LGA 和巨大儿的多变量预测模型，评估纳入模型在临床实践中的性能、质量和适用性。
截至 2022 年 6 月，检索了 MEDLINE、EMBASE 和 Cochrane 图书馆。
我们纳入了报告胎儿巨大儿和/或 LGA 的任何多变量预测模型的开发和/或验证的观察性和实验性研究。我们排除了仅使用单一变量或未评估模型性能的研究。
使用预测模型研究系统评价的关键评估和数据提取清单（Checklist for critical appraisal and data extraction for systematic reviews of prediction modelling studies checklist）提取数据。提取模型性能衡量指标包括区分度、校准和验证。通过其对 TRIPOD（个体预后或诊断的多变量预测模型的透明报告）清单的遵守情况评估每个研究的报告质量和完整性。使用 PROBAST（预测模型风险评估工具）测量偏倚风险和适用性。
共确定了 8442 条引文，其中 58 条被纳入分析：32/58（55.2%）开发、21/58（36.2%）开发和内部验证、2/58（3.4%）开发和外部验证模型。只有三项研究对外验证了现有模型。许多研究对巨大儿和 LGA 进行了不同的定义。总共使用 112 个不同变量开发了 111 个多变量预测模型。模型的区分度范围很广（受试者工作特征曲线下面积 0.56-0.96），很少有研究报告校准（58 篇中的 11 篇，19.0%）。只有 5/58（8.6%）的研究具有低偏倚风险。
目前没有用于巨大儿/LGA 的多变量预测模型可以直接用于临床。
