Are We Accurately Predicting Mortality in Renal Cancer? A Systematic Review of Prognostic Models

Background/Objectives: Renal cancer has a heterogeneous characteristic. Prognostic models can enable a better evaluation of the prognosis. This study aimed to analyze the clinical applicability and risk of bias of prognostic models described in the literature for predicting cancer-specific mortality in renal cancer patients who have undergone nephrectomy. Methods: A systematic review (PROSPERO [CRD42021243529]) of all scientific articles that evaluate prognostic models for cancer-specific mortality due to renal cancer was performed. Descriptive analysis and application of the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) and the Prediction Model Risk Of Bias Assessment Tool (PROBAST) were used. Results: Most of the 40 reviewed studies used retrospective cohort designs, mainly based on hospital records or the SEER database, and focused on patients undergoing nephrectomy. While 50% developed models without validation, the rest included internal or external validation methods, with nomograms being the most common format for presenting results. Cox regression was the main modeling technique, although problems such as poor treatment of missing data, inadequate reporting of events per variable, and limited assessment of model assumptions were prevalent. According to the PROBAST assessment, all studies showed a high risk of bias, particularly in the scope of analysis, and only 40% had good applicability. Conclusions: All the studies analyzed were found to have a high risk of bias, and only 40% demonstrated good applicability. Hence, it is necessary to develop cancer-specific prognostic models for renal cancer based on the CHARMS and PROBAST frameworks.