Predictive Models of Morbidity and Mortality in Gynaecological Cancer Surgery: A Systematic Review

Background/Objectives: Gynaecological–oncological surgery involves substantial perioperative morbidity and mortality risks. Reliable preoperative risk stratification could inform consent, shared decision-making and perioperative optimisation. Several prediction models exist, but their performance and methodological quality in this population remain uncertain. We aimed to provide a comprehensive synthesis of existing evidence to guide the selection and development of prediction tools in this population.

Methods

We searched MEDLINE and EMBASE using a prospective protocol (PROSPERO CRD420251179495) and included studies developing or validating multivariable prediction models or risk scores for graded postoperative morbidity/mortality after gynaecological–oncological surgery. Risk of bias and applicability were assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST) at the study (publication) level. Findings were synthesised narratively.