Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 May 26;16(11):2021.
doi: 10.3390/cancers16112021.

Predicting Risk of Post-Operative Morbidity and Mortality following Gynaecological Oncology Surgery (PROMEGO): A Global Gynaecological Oncology Surgical Outcomes Collaborative Led Study

Faiza Gaba  1   2 , Sara Mahvash Mohammadi  3 , Mikhail I Krivonosov  4   5 , Oleg Blyuss  3   6 ; GO SOAR Collaborators
Collaborators, Affiliations

Predicting Risk of Post-Operative Morbidity and Mortality following Gynaecological Oncology Surgery (PROMEGO): A Global Gynaecological Oncology Surgical Outcomes Collaborative Led Study

Faiza Gaba et al. Cancers (Basel). .

Abstract

The medical complexity of surgical patients is increasing, and surgical risk calculators are crucial in providing high-value, patient-centered surgical care. However, pre-existing models are not validated to accurately predict risk for major gynecological oncology surgeries, and many are not generalizable to low- and middle-income country settings (LMICs). The international GO SOAR database dataset was used to develop a novel predictive surgical risk calculator for post-operative morbidity and mortality following gynecological surgery. Fifteen candidate features readily available pre-operatively across both high-income countries (HICs) and LMICs were selected. Predictive modeling analyses using machine learning methods and linear regression were performed. The area-under-the-receiver-operating characteristic curve (AUROC) was calculated to assess overall discriminatory performance. Neural networks (AUROC 0.94) significantly outperformed other models (p < 0.001) for evaluating the accuracy of prediction across three groups, i.e., minor morbidity (Clavien-Dindo I-II), major morbidity (Clavien-Dindo III-V), and no morbidity. Logistic-regression modeling outperformed the clinically established SORT model in predicting mortality (AUROC 0.66 versus 0.61, p < 0.001). The GO SOAR surgical risk prediction model is the first that is validated for use in patients undergoing gynecological surgery. Accurate surgical risk predictions are vital within the context of major cytoreduction surgery, where surgery and its associated complications can diminish quality-of-life and affect long-term cancer survival. A model that requires readily available pre-operative data, irrespective of resource setting, is crucial to reducing global surgical disparities.

Keywords: machine learning; surgical morbidity; surgical mortality; surgical risk calculator.

PubMed Disclaimer

Conflict of interest statement

F.G. declares funding from the NHS Grampian Endowment Fund, Medtronic, Karl Storz, the British Gynaecological Cancer Society outside of this work, and an honorarium from Astra Zeneca. M.I.K. declares funding in the field of artificial intelligence, provided by the Analytical Center for the Government of the Russian Federation, in accordance with the subsidy agreement (agreement identifier 000000D730321P5Q0002) and the agreement with the Ivannikov Institute for System Programming of the Russian Academy of Sciences dated 2 November 2021, No. 70-2021-00142. O.B. declares funding from Barts Charity (G-001522). All other authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Heatmap of the confusion matrix of the performance of the neural networks. Shaded areas represent accuracy of respective true outcome.
Figure 2
Figure 2
Distribution of the AUROC and sensitivities values across 1000 splits for the logistic regression and SORT.
See this image and copyright information in PMC

References

    1. Talmor D., Kelly B. How to better identify patients at high risk of postoperative complications? Curr. Opin. Crit. Care. 2017;23:417–423. doi: 10.1097/mcc.0000000000000445. - DOI - PubMed
    1. van Schaik J., Hers T.M., van Rijswijk C.S., Schooneveldt M.S., Putter H., Eefting D., van der Vorst J.R. Risk assessment in aortic aneurysm repair by medical specialists versus the American College of Surgeons National Surgical Quality Improvement Program risk calculator outcomes. JRSM Cardiovasc. Dis. 2021;10:20480040211006582. doi: 10.1177/20480040211006582. - DOI - PMC - PubMed
    1. El Moheb M., Gebran A., Maurer L.R., Naar L., El Hechi M., Breen K., Dorken-Gallastegi A., Sinyard R., Bertsimas D., Velmahos G., et al. Artificial intelligence versus surgeon gestalt in predicting risk of emergency general surgery. J. Trauma Acute Care Surg. 2023;95:565–572. doi: 10.1097/ta.0000000000004030. - DOI - PubMed
    1. Pellisé F., Vila-Casademunt A., Núñez-Pereira S., Haddad S., Smith J.S., Kelly M.P., Alanay A., Shaffrey C., Pizones J., Yilgor Ç., et al. Surgeons’ risk perception in ASD surgery: The value of objective risk assessment on decision making and patient counselling. Eur. Spine J. 2022;31:1174–1183. doi: 10.1007/s00586-022-07166-2. - DOI - PubMed
    1. Podrat J.L., Del Val F.R., Pei K.Y. Evolution of Risk Calculators and the Dawn of Artificial Intelligence in Predicting Patient Complications. Surg. Clin. N. Am. 2021;101:97–107. doi: 10.1016/j.suc.2020.08.012. - DOI - PubMed

LinkOut - more resources