Machine Learning-Based Risk Prediction of Critical Care Unit Admission for Advanced Stage High Grade Serous Ovarian Cancer Patients Undergoing Cytoreductive Surgery: The Leeds-Natal Score



Laios, Alexandros, De Oliveira Silva, Raissa Vanessa, Dantas De Freitas, Daniel Lucas, Tan, Yong Sheng, Saalmink, Gwendolyn, Zubayraeva, Albina, Johnson, Racheal, Kaufmann, Angelika, Otify, Mohammed ORCID: 0000-0002-7884-8680, Hutson, Richard
et al (show 6 more authors) (2022) Machine Learning-Based Risk Prediction of Critical Care Unit Admission for Advanced Stage High Grade Serous Ovarian Cancer Patients Undergoing Cytoreductive Surgery: The Leeds-Natal Score. JOURNAL OF CLINICAL MEDICINE, 11 (1). 87-.

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Abstract

Achieving complete surgical cytoreduction in advanced stage high grade serous ovarian cancer (HGSOC) patients warrants an availability of Critical Care Unit (CCU) beds. Machine Learning (ML) could be helpful in monitoring CCU admissions to improve standards of care. We aimed to improve the accuracy of predicting CCU admission in HGSOC patients by ML algorithms and developed an ML-based predictive score. A cohort of 291 advanced stage HGSOC patients with fully curated data was selected. Several linear and non-linear distances, and quadratic discriminant ML methods, were employed to derive prediction information for CCU admission. When all the variables were included in the model, the prediction accuracies were higher for linear discriminant (0.90) and quadratic discriminant (0.93) methods compared with conventional logistic regression (0.84). Feature selection identified pre-treatment albumin, surgical complexity score, estimated blood loss, operative time, and bowel resection with stoma as the most significant prediction features. The real-time prediction accuracy of the Graphical User Interface CCU calculator reached 95%. Limited, potentially modifiable, mostly intra-operative factors contributing to CCU admission were identified and suggest areas for targeted interventions. The accurate quantification of CCU admission patterns is critical information when counseling patients about peri-operative risks related to their cytoreductive surgery.

Item Type: Article
Uncontrolled Keywords: Machine Learning, surgical cytoreduction, Critical Care Unit, ovarian cancer, Graphical User Interface
Divisions: Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Clinical Directorate
Depositing User: Symplectic Admin
Date Deposited: 08 Dec 2022 10:50
Last Modified: 18 Jan 2023 19:41
DOI: 10.3390/jcm11010087
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3166553