Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
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
_version_ 1784630365270310912
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
work_keys_str_mv AT laiosalexandros machinelearningbasedriskpredictionofcriticalcareunitadmissionforadvancedstagehighgradeserousovariancancerpatientsundergoingcytoreductivesurgerytheleedsnatalscore
AT deoliveirasilvaraissavanessa machinelearningbasedriskpredictionofcriticalcareunitadmissionforadvancedstagehighgradeserousovariancancerpatientsundergoingcytoreductivesurgerytheleedsnatalscore
AT dantasdefreitasdaniellucas machinelearningbasedriskpredictionofcriticalcareunitadmissionforadvancedstagehighgradeserousovariancancerpatientsundergoingcytoreductivesurgerytheleedsnatalscore
AT tanyongsheng machinelearningbasedriskpredictionofcriticalcareunitadmissionforadvancedstagehighgradeserousovariancancerpatientsundergoingcytoreductivesurgerytheleedsnatalscore
AT saalminkgwendolyn machinelearningbasedriskpredictionofcriticalcareunitadmissionforadvancedstagehighgradeserousovariancancerpatientsundergoingcytoreductivesurgerytheleedsnatalscore
AT zubayraevaalbina machinelearningbasedriskpredictionofcriticalcareunitadmissionforadvancedstagehighgradeserousovariancancerpatientsundergoingcytoreductivesurgerytheleedsnatalscore
AT johnsonracheal machinelearningbasedriskpredictionofcriticalcareunitadmissionforadvancedstagehighgradeserousovariancancerpatientsundergoingcytoreductivesurgerytheleedsnatalscore
AT kaufmannangelika machinelearningbasedriskpredictionofcriticalcareunitadmissionforadvancedstagehighgradeserousovariancancerpatientsundergoingcytoreductivesurgerytheleedsnatalscore
AT otifymohammed machinelearningbasedriskpredictionofcriticalcareunitadmissionforadvancedstagehighgradeserousovariancancerpatientsundergoingcytoreductivesurgerytheleedsnatalscore
AT hutsonrichard machinelearningbasedriskpredictionofcriticalcareunitadmissionforadvancedstagehighgradeserousovariancancerpatientsundergoingcytoreductivesurgerytheleedsnatalscore
AT thangaveluamudha machinelearningbasedriskpredictionofcriticalcareunitadmissionforadvancedstagehighgradeserousovariancancerpatientsundergoingcytoreductivesurgerytheleedsnatalscore
AT broadheadtim machinelearningbasedriskpredictionofcriticalcareunitadmissionforadvancedstagehighgradeserousovariancancerpatientsundergoingcytoreductivesurgerytheleedsnatalscore
AT nugentdavid machinelearningbasedriskpredictionofcriticalcareunitadmissionforadvancedstagehighgradeserousovariancancerpatientsundergoingcytoreductivesurgerytheleedsnatalscore
AT theophilougeorgios machinelearningbasedriskpredictionofcriticalcareunitadmissionforadvancedstagehighgradeserousovariancancerpatientsundergoingcytoreductivesurgerytheleedsnatalscore
AT gomesdelimakassiomichell machinelearningbasedriskpredictionofcriticalcareunitadmissionforadvancedstagehighgradeserousovariancancerpatientsundergoingcytoreductivesurgerytheleedsnatalscore
AT dejongdiederick machinelearningbasedriskpredictionofcriticalcareunitadmissionforadvancedstagehighgradeserousovariancancerpatientsundergoingcytoreductivesurgerytheleedsnatalscore