Predictive Models of Morbidity and Mortality in Gynaecological Cancer Surgery: A Systematic Review
4 affiliations
- 1Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London E1 2AD, UK
- 2Department of Anaesthesia, Pain and Peri-Operative Medicine, Royal London Hospital, Barts Health NHS Trust, London E1 1FR, UK
- 3Department of Gynaecological Oncology, Royal London Hospital, Barts Health NHS Trust, London E1 1FR, UK
- 4Wolfson Institute of Population Health, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
Abstract
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.
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BibTeX
@article{fernandes2026predictive,
title = {{Predictive Models of Morbidity and Mortality in Gynaecological Cancer Surgery: A Systematic Review}},
author = {Katie Avelina Fernandes and Sonu Krishel Vidanagamage and Mevan Gooneratne and Robert Wilkinson and Madeleine Wells and Ching Ling Pang and Shaun May and Georgia Alexandra Elizabeth Ellis and Elly Brockbank and Arjun Jeyarajah and Alexandra Lawrence and Saurabh Phadnis and Ranjit Manchanda and Samuel George Oxley and Michail Sideris},
journal = {Journal of Clinical Medicine},
year = {2026},
month = oct,
doi = {10.3390/jcm15207948},
url = {https://www.mdpi.com/2077-0383/15/20/7948},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Predictive Models of Morbidity and Mortality in Gynaecological Cancer Surgery: A Systematic Review
AU - Katie Avelina Fernandes
AU - Sonu Krishel Vidanagamage
AU - Mevan Gooneratne
AU - Robert Wilkinson
AU - Madeleine Wells
AU - Ching Ling Pang
AU - Shaun May
AU - Georgia Alexandra Elizabeth Ellis
AU - Elly Brockbank
AU - Arjun Jeyarajah
AU - Alexandra Lawrence
AU - Saurabh Phadnis
AU - Ranjit Manchanda
AU - Samuel George Oxley
AU - Michail Sideris
PY - 2026
DA - 2026/10/01
JO - Journal of Clinical Medicine
DO - 10.3390/jcm15207948
UR - https://www.mdpi.com/2077-0383/15/20/7948
AB - 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.
SN - 2077-0383
ER -