Appraisal of multivariable prognostic models for post-operative liver decompensation following partial hepatectomy: a systematic review

Abstract

Presenter: Zuhaib Mir MD, MSc | Queen's University Background: Few reports have evaluated prognostic modelling studies of tools used for surgical decision-making. This systematic review aimed to describe and critically appraise studies that have developed or validated multivariable prognostic models for post-operative liver decompensation following partial hepatectomy. Methods: This study was designed using the CHARMS checklist. Following a comprehensive literature search, two reviewers independently screened candidate references for inclusion and abstracted relevant study details. Studies were excluded if their objective was predictor-finding, if they only assessed the prognostic value of a single factor (unless adding to a pre-existing multivariable model), if they had an inapplicable analytic purpose (eg. multivariable modelling not aimed at prognostication, development of novel statistical methods), if their outcome(s) did not include a post-operative liver decompensation event, or if they were a duplicate study not initially screened out. Qualitative assessment was performed using the PROBAST tool. Results: We identified 36 prognostic modelling studies; 25 focused on development only, 3 developed and validated models, and 8 validated pre-existing models. None compared routine use of a prognostic model against standard clinical practice. Most studies used single-institution, retrospective cohort designs, predominantly conducted in Eastern populations. In total, 15 different outcome definitions for post-operative liver decompensation events were used. Statistical concerns surrounding model overfitting, performance assessment, and internal validation led to high risk of bias for all studies. Conclusion: Current prognostic models for post-operative liver decompensation following partial hepatectomy may not be valid for routine clinical use due to design and methodologic concerns. Landmark resources and reporting guidelines such as the TRIPOD statement may assist researchers, and additionally, model impact assessment studies represent opportunities for future research.