中文期刊发表的预测模型系统评价文献调查与评价: 方法学质量和报告质量

王子怡; 卢存存; 黄家艺; 张晶磊; 尚文茹; 崔璐; 刘文迪; 邓秀秀; 赵晓晓; 杨克虎; 李秀霞

doi:10.12290/xhyxzz.2023-0418

中文期刊发表的预测模型系统评价文献调查与评价: 方法学质量和报告质量

Investigation and Evaluation of Systematic Reviews of Prediction Models Published in Chinese Journals: Methodological and Reporting Quality

摘要

摘要:
目的评价中文期刊发表的预测模型系统评价文献的方法学质量和报告质量，为提高我国预测模型系统评价的整体质量提供依据。
方法计算机检索中国知网、万方数据知识服务平台、中国生物医学文献数据库和维普数据库，获取自建库至2023年7月20日发表的预测模型系统评价相关文献。由2名研究者独立筛选文献、提取资料后，采用AMSTAR(A Measurement Tool to Assess Systematic Reviews)和PRISMA 2020(Preferred Reporting Items for Systematic reviews and Meta-Analyses 2020)分别评价纳入的系统评价文献方法学质量和报告质量。
结果共纳入发表于2015— 2023年的55篇系统评价文献，其中12篇为Meta分析，最常见的研究主题为心血管疾病、脑卒中和糖尿病。预测模型系统评价文献的方法学质量需改进的内容主要涉及AMSTAR的条目1、4、5、6和10，报告质量需提高的内容主要涉及PRISMA 2020的条目7、10a、12、13a-f、14、15、16a-b, 17、20b-d、21、22、23d、24a-c、25和26。纳入的系统评价文献方法学质量与报告质量具有中等程度的正相关性(r=0.58，P＜0.001)。多重线性回归分析表明，较长的篇幅、近期发表和受到基金资助与更高的方法学质量相关(P＜0.05)；较长的篇幅、近期发表、发表为定性系统评价和受到基金资助与更高的报告质量相关，但更多的作者却与更低的报告质量相关(P＜0.05)。
结论当前中文期刊发表的预测模型系统评价的方法学质量和报告质量整体较低，尚有待提高。

Abstract:
Objective To analyze the methodological and reporting quality of systematic reviews of prediction models published in Chinese journals, with the aim of providing reference for enhancing the overall quality of Chinese systematic reviews of prediction models.
Methods We searched the CNKI, WanFang Data, CBM, and VIP databases for Chinese systematic reviews of prediction models from inception to July 20, 2023. After two independent reviewers screened literature and extracted data, the AMSTAR(A Measurement Tool to Assess Systematic Reviews) and PRISMA 2020(Preferred Reporting Items for Systematic reviews and Meta-Analyses 2020) tools were used to assess the methodological and reporting quality of the included reviews.
Results A total of 55 systematic reviews published between 2015 and 2023 were included, 12 of which were meta-analysis. The reviews covered various topics, mainly including cardiovascular diseases, stroke, and diabetes. The identified systematic reviews exhibited obvious deficiencies: items 1, 4, 5, 6, and 10 of AMSTAR showed poor methodological quality, and items 7, 10a, 12, 13a-f, 14, 15, 16a-b, 17, 20b-d, 21, 22, 23d, 24a-c, 25 and 26 of PRISMA 2020 needed improvement in reporting quality. Furthermore, a moderate positive correlation (r=0.58, P < 0.001) was observed between the methodological and reporting quality. Multiple linear regression analysis revealed that a greater number of pages, more recent publications, and funding support were associated with higher methodological quality (P < 0.05). Similarly, a greater number of pages, more recent publications, qualitative systematic reviews, and funding support were associated with higher reporting quality, but the number of authors showed a negative association (P < 0.05).
Conclusion The methodological and reporting quality of existing systematic reviews of prediction models published in Chinese journals is relatively poor and demands improvement.

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