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Category: Project

Description: Background: Clinical prediction models for prognosis can be used to predict outcomes and support decision-making. Previous research criticized the quality of prediction models concerning poor reporting and the risk of bias. How this applies to prediction models in organ donation and transplantation needs to be clarified. Therefore, this scoping review aims to assess prediction models used in transplant centers in Switzerland and update clinicians on the transparency, quality of reporting, and risk of bias of these tools. Methods: We will use expert interviews and a survey with transplant clinicians to collect prediction models used in clinical practice in the context of organ donation and transplantation at Swiss transplant centers. We will apply the reporting guideline for transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) and the prediction model risk of bias assessment tool (PROBAST). The scoping review will report on whether prediction models were transparently reported, the risk of bias, and concerns regarding its applicability. Discussion: Assessing clinical prediction models can give clinicians important context when using risk calculators in clinical decision-making. Moreover, by assessing the limitations of existing prediction models, this study can inform further research to develop novel or update existing models to improve the decision-making at transplant centers. We expect to find clinical prediction models based on machine learning models, and their evaluation regarding reporting quality and risk of bias assessment will pose specific challenges.