Abstract

The clinical application of prediction models is increasing within the field of gynaecology and obstetrics. This is mostly due to the fact that clinicians and patients prefer individualized counselling and person specific, more objective outcome assessment. To prevent using inadequate models, it is important to construct and perform prediction model studies correctly. Therefore, the TRIPOD statement (the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) was developed. The aim of this review is to obtain an overview of the existing published prediction models for benign gynaecology and to investigate to what extent these studies meet the TRIPOD criteria.We performed a literature search in the databases PubMed, Embase and Cochrane Library from inception to August 2020. Searching the cross-references of the relevant studies within our search identified additional articles. Publications were included if the aim of the study was to develop a multivariable prediction model within the field of benign gynaecology. Two independent reviewers extracted the data. Analysis of the studies was performed by using a checklist derived from the TRIPOD criteria.Based on our search, 2487 studies were selected, including potential duplications. Eventually, a total of twenty-two studies were selected. 91% of these studies handled their predictors by univariable analysis before developing a multivariable prediction model. Fifteen studies described having missing data, but not all of them (9%) handled these missing data. Four different internal validation methods were used in twenty studies. Fifteen studies (68%) had prediction models with a C-index ≥ 0.7, which indicates a good model. Half of the studies (50%) did not measure the calibration, overall performance was described in two studies (9%). External validation was performed in 9% of the studies.The correct development of a prediction model within benign gynaecology and subsequent transparent reporting of the model development is important to facilitate clinical use. Without transparent reporting, wrong assumptions can be made leading to incorrect application of a specific prediction model. This overview shows that excepting carrying out an external validation, only one article met all the criteria. Therefore, we strongly recommend use of the TRIPOD criteria for developing and validating a prediction model (study). In addition, prior to publication, content experts should critically and statistically review the prediction model. If too many criteria are not met, refusing publication should be considered.

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