Prediction models for living organ transplantation are poorly developed, reported and validated: a systematic review
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Article de revue
This item was published in
Journal of Clinical Epidemiology. 2022-05, vol. 145, p. 126-135
English Abstract
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 ...Read more >
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.Read less <
English Keywords
Risk prediction models
Living donor
Kidney transplantation
Liver transplantation
Systematic review
Risk of bias
Quality of reporting