Prediction models for severe treatment-related toxicities in older adults with cancer: a systematic review

Abstract

Background Ageing increases the risk of treatment-related toxicities (TRT) in patients with cancer. This systematic review provided an overview of existing prediction models for TRT in this population and evaluated their predictive performances. Methods A systematic search was conducted in MEDLINE (Ovid), Embase, PubMed, CINAHL and CENTRAL (Cochrane Central Register of Controlled Trials) databases for studies developing severe TRT prediction models in older cancer patients published between 1 January 2000 and 31 October 2023. The included models were summarised and assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Results Out of the 6192 studies identified through literature searching, 12 studies involving 90 819 participants met the inclusion criteria. About 15 prediction models (9 (60%) for diverse cancer types; 6 (40%) for specific cancer types) were analysed. The models included between 4 and 11 variables. The most common predictors were physical function (n = 12, 80%), performance status (n = 5, 33.3%) and the MAX2 index (n = 5, 33.3%). About 2 models (13.3%) had external validation, 9 (60.0%) had internal validation and 6 (40.0%) lacked any validation. All studies were assessed to have a high risk of bias according to the PROBAST criteria. Conclusion This systematic review demonstrated that existing prediction models for TRT exhibited moderate discrimination ability in older patients with cancer, with significant heterogeneity in clinical settings and predictive variables. Standardised procedures for developing and validating prediction models are essential to improve the prediction of severe TRT in this vulnerable population.