The reporting of prognostic prediction models for obstetric care was poor: a cross-sectional survey of 10-year publications

Author:

Liu Chunrong,Qi Yana,Liu Xinghui,Chen Meng,Xiong Yiquan,Huang Shiyao,Zou Kang,Tan Jing,Sun Xin

Abstract

Abstract Background To investigate the reporting of prognostic prediction model studies in obstetric care through a cross-sectional survey design. Methods PubMed was searched to identify prognostic prediction model studies in obstetric care published from January 2011 to December 2020. The quality of reporting was assessed by the TRIPOD checklist. The overall adherence by study and the adherence by item were calculated separately, and linear regression analysis was conducted to explore the association between overall adherence and prespecified study characteristics. Results A total of 121 studies were included, while no study completely adhered to the TRIPOD. The results showed that the overall adherence was poor (median 46.4%), and no significant improvement was observed after the release of the TRIPOD (43.9 to 46.7%). Studies including both model development and external validation had higher reporting quality versus those including model development only (68.1% vs. 44.8%). Among the 37 items required by the TRIPOD, 10 items were reported adequately with an adherence rate over of 80%, and the remaining 27 items had an adherence rate ranging from 2.5 to 79.3%. In addition, 11 items had a report rate lower than 25.0% and even covered key methodological aspects, including blinding assessment of predictors (2.5%), methods for model-building procedures (4.5%) and predictor handling (13.5%), how to use the model (13.5%), and presentation of model performance (14.4%). Conclusions In a 10-year span, prognostic prediction studies in obstetric care continued to be poorly reported and did not improve even after the release of the TRIPOD checklist. Substantial efforts are warranted to improve the reporting of obstetric prognostic prediction models, particularly those that adhere to the TRIPOD checklist are highly desirable.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

China Medical Board

Sichuan Youth Science and Technology Innovation Research Team

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Epidemiology

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