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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
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...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8745521/ https://www.ncbi.nlm.nih.gov/pubmed/35011828 http://dx.doi.org/10.3390/jcm11010087 |
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author | 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 Hutson, Richard Thangavelu, Amudha Broadhead, Tim Nugent, David Theophilou, Georgios Gomes de Lima, Kassio Michell De Jong, Diederick |
author_facet | 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 Hutson, Richard Thangavelu, Amudha Broadhead, Tim Nugent, David Theophilou, Georgios Gomes de Lima, Kassio Michell De Jong, Diederick |
author_sort | Laios, Alexandros |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8745521 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87455212022-01-11 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 Hutson, Richard Thangavelu, Amudha Broadhead, Tim Nugent, David Theophilou, Georgios Gomes de Lima, Kassio Michell De Jong, Diederick J Clin Med Article 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. MDPI 2021-12-24 /pmc/articles/PMC8745521/ /pubmed/35011828 http://dx.doi.org/10.3390/jcm11010087 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article 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 Hutson, Richard Thangavelu, Amudha Broadhead, Tim Nugent, David Theophilou, Georgios Gomes de Lima, Kassio Michell De Jong, Diederick 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 |
title | 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 |
title_full | 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 |
title_fullStr | 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 |
title_full_unstemmed | 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 |
title_short | 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 |
title_sort | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8745521/ https://www.ncbi.nlm.nih.gov/pubmed/35011828 http://dx.doi.org/10.3390/jcm11010087 |
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