Article type
Abstract
"Background: Systematic reviews of prediction models can assess the risk of bias and applicability of multiple models targeting same outcome variable, thus providing recommendations for clinical decision-making. However, the overall quality of these system reviews published in Chinese journals are unclear, which has affected widespread utilization of their research findings to guide clinical practice.
Objective: To analyze the methodological and reporting quality of systematic reviews of prediction models published in Chinese journals, with the aim of providing references for enhancing the quality of Chinese systematic reviews of prediction models.
Methods: Chinese systematic reviews of prediction models were electronically searched in CNKI, WanFang Data, CBM, and VIP databases from inception to July 20, 2023. Two independent reviewers screened literature, extracted data, and used the A Measurement Tool to Assess Systematic Reviews (AMSTAR) and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 tools to assess methodological and reporting quality of the included reviews.
Results: A total of 55 systematic reviews published between 2015 and 2023 were included, with 12 of them being meta-analyses. These reviews encompassed a range of topics, with a primary focus on cardiovascular diseases, stroke, and diabetes. The identified systematic reviews exhibited obvious deficiencies in some areas, including items 1, 4, 5, 6, and 10 of AMSTAR, as well as items 7, 10a, 12, 13a-f, 14, 15, 16a-b, 17, 20b-d, 21, 22, 23d, 24a-c, 25 and 26 of PRISMA 2020. Furthermore, a moderate positive correlation (r = 0.58, P < 0.001) was observed between the methodological and reporting quality. Multiple linear regression analyses revealed: greater number of pages, more recent publications, and funding support were associated with higher methodological quality (P < 0.05) (Figure 1). Similarly, 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) (Figure 2).
Conclusions The current systematic reviews of prediction models published in Chinese journals require enhancement in both methodological and reporting quality.
Funding statement: This work was supported by the Fundamental Research Funds for the Central Universities [Grant number: lzujbky-2021-ct06,lzujbky-2021-kb22]."
Objective: To analyze the methodological and reporting quality of systematic reviews of prediction models published in Chinese journals, with the aim of providing references for enhancing the quality of Chinese systematic reviews of prediction models.
Methods: Chinese systematic reviews of prediction models were electronically searched in CNKI, WanFang Data, CBM, and VIP databases from inception to July 20, 2023. Two independent reviewers screened literature, extracted data, and used the A Measurement Tool to Assess Systematic Reviews (AMSTAR) and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 tools to assess methodological and reporting quality of the included reviews.
Results: A total of 55 systematic reviews published between 2015 and 2023 were included, with 12 of them being meta-analyses. These reviews encompassed a range of topics, with a primary focus on cardiovascular diseases, stroke, and diabetes. The identified systematic reviews exhibited obvious deficiencies in some areas, including items 1, 4, 5, 6, and 10 of AMSTAR, as well as items 7, 10a, 12, 13a-f, 14, 15, 16a-b, 17, 20b-d, 21, 22, 23d, 24a-c, 25 and 26 of PRISMA 2020. Furthermore, a moderate positive correlation (r = 0.58, P < 0.001) was observed between the methodological and reporting quality. Multiple linear regression analyses revealed: greater number of pages, more recent publications, and funding support were associated with higher methodological quality (P < 0.05) (Figure 1). Similarly, 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) (Figure 2).
Conclusions The current systematic reviews of prediction models published in Chinese journals require enhancement in both methodological and reporting quality.
Funding statement: This work was supported by the Fundamental Research Funds for the Central Universities [Grant number: lzujbky-2021-ct06,lzujbky-2021-kb22]."