Key result
Gynecological cancer surgery risk models show moderate discrimination, limited calibration, and rare external validation.
Why the study?
The performance and methodological quality of existing prediction models for morbidity and mortality after gynaecological–oncological surgery remain uncertain.
Do existing multivariable prediction models accurately predict graded postoperative morbidity and mortality in patients undergoing gynaecological-oncological surgery?
Systematic Review (n=75,999)
Do existing multivariable prediction models accurately predict graded postoperative morbidity and mortality in patients undergoing gynaecological-oncological surgery?
Current preoperative risk prediction models for gynaecological-oncological surgery exhibit methodological flaws and inadequate external validation, precluding their recommendation for routine clinical practice.
No existing model warrants routine use in gynaecological oncology; confirms high bias and limited accuracy, guiding development of validated tools.
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. Results: Twenty-eight studies (75,999 patient records, including overlapping cohorts) were included (23 retrospective, five prospective; sample sizes 100–21,359). Models were multifactorial (n = 17), including generic and gynaecological–oncological-specific tools, frailty-based (n = 8) models or biomarker and index-based (n = 3) models. A numerical c-statistic was reported in 20 studies (binary c-statistics 0.50–0.92 across heterogeneous outcome definitions), calibration was reported in nine studies, and internal validation was reported in thirteen. No frailty-based model reported calibration or validation. One model specific to ovarian cytoreductive surgery was independently externally validated, retaining discrimination (c-index 0.715) with reduced calibration. Generic tools performed poorly, (c-statistics ranged from 0.529-0.644). Twenty studies were at high risk of bias, predominantly within the analysis domain. Conclusions: Existing models show, at best, moderate discrimination, limited calibration and infrequent external validation within the gynaecological–oncological surgical population. No model can currently be recommended for routine clinical practice. Further independent external validation of specialty-specific models in local settings, with full calibration reporting, is needed to support preoperative counselling.
No takes yet. Share an insight, caveat, or question.
Fernandes et al. (2026) conducted a systematic review in Gynaecological cancer surgery (n=75,999). Preoperative multivariable prediction models and risk scores was evaluated on Predictive model performance for graded postoperative morbidity/mortality. Existing preoperative prediction models for gynaecological cancer surgery showed moderate discrimination (c-statistics 0.50-0.92), limited calibration, and infrequent external validation.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: