Prediction models for living organ transplantation are poorly developed, reported, and validated: a systematic review

Autor: Maria C. Haller, Constantin Aschauer, Christine Wallisch, Karen Leffondré, Maarten van Smeden, Rainer Oberbauer, Georg Heinze
Přispěvatelé: Admin, Oskar, Medizinische Universität Wien = Medical University of Vienna, Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), University Medical Center [Utrecht]
Rok vydání: 2022
Předmět:
Zdroj: Journal of Clinical Epidemiology
Journal of Clinical Epidemiology, Elsevier, 2022, 145, pp.126-135. ⟨10.1016/j.jclinepi.2022.01.025⟩
ISSN: 0895-4356
Popis: International audience; OBJECTIVE: To identify and critically appraise risk prediction models for living donor solid organ transplant counselling.STUDY DESIGN AND SETTING: We systematically reviewed articles describing the development or validation of prognostic risk prediction models about living donor solid organ (kidney and liver) transplantation indexed in Medline until April 4(th) 2021. Models were eligible if intended to predict, at transplant counselling, any outcome occurring after transplantation or donation in recipients or donors. Duplicate study selection, data extraction, assessment for risk of bias and quality of reporting was done using the CHARMS checklist, PRISMA recommendations, PROBAST tool, and TRIPOD Statement.RESULTS: We screened 4691 titles and included 49 studies describing 68 models (35 kidney, 33 liver transplantation). We identified 49 new risk prediction models and 19 external validations of existing models. Most models predicted recipients outcomes (n=38, 75%), e.g., kidney graft loss (29%), or mortality of liver transplant recipients (55%). Many new models (n= 46, 94%) and external validations (n=17, 89%) had a high risk of bias because of methodological weaknesses. The quality of reporting was generally poor.CONCLUSION: We advise against applying poorly developed, reported or validated prediction models. Future studies could validate or update the few identified methodologically appropriate models.
Databáze: OpenAIRE