Cargando…

Machine Learning Prediction of Length of Stay in Adult Spinal Deformity Patients Undergoing Posterior Spine Fusion Surgery

(1) Background: Length of stay (LOS) is a commonly reported metric used to assess surgical success, patient outcomes, and economic impact. The focus of this study is to use a variety of machine learning algorithms to reliably predict whether a patient undergoing posterior spinal fusion surgery treat...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Andrew S, Veeramani, Ashwin, Quinn, Matthew S., Alsoof, Daniel, Kuris, Eren O., Daniels, Alan H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8471961/
https://www.ncbi.nlm.nih.gov/pubmed/34575182
http://dx.doi.org/10.3390/jcm10184074
_version_ 1784574601982902272
author Zhang, Andrew S
Veeramani, Ashwin
Quinn, Matthew S.
Alsoof, Daniel
Kuris, Eren O.
Daniels, Alan H.
author_facet Zhang, Andrew S
Veeramani, Ashwin
Quinn, Matthew S.
Alsoof, Daniel
Kuris, Eren O.
Daniels, Alan H.
author_sort Zhang, Andrew S
collection PubMed
description (1) Background: Length of stay (LOS) is a commonly reported metric used to assess surgical success, patient outcomes, and economic impact. The focus of this study is to use a variety of machine learning algorithms to reliably predict whether a patient undergoing posterior spinal fusion surgery treatment for Adult Spine Deformity (ASD) will experience a prolonged LOS. (2) Methods: Patients undergoing treatment for ASD with posterior spinal fusion surgery were selected from the American College of Surgeon’s NSQIP dataset. Prolonged LOS was defined as a LOS greater than or equal to 9 days. Data was analyzed with the Logistic Regression, Decision Tree, Random Forest, XGBoost, and Gradient Boosting functions in Python with the Sci-Kit learn package. Prediction accuracy and area under the curve (AUC) were calculated. (3) Results: 1281 posterior patients were analyzed. The five algorithms had prediction accuracies between 68% and 83% for posterior cases (AUC: 0.566–0.821). Multivariable regression indicated that increased Work Relative Value Units (RVU), elevated American Society of Anesthesiologists (ASA) class, and longer operating times were linked to longer LOS. (4) Conclusions: Machine learning algorithms can predict if patients will experience an increased LOS following ASD surgery. Therefore, medical resources can be more appropriately allocated towards patients who are at risk of prolonged LOS.
format Online
Article
Text
id pubmed-8471961
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-84719612021-09-28 Machine Learning Prediction of Length of Stay in Adult Spinal Deformity Patients Undergoing Posterior Spine Fusion Surgery Zhang, Andrew S Veeramani, Ashwin Quinn, Matthew S. Alsoof, Daniel Kuris, Eren O. Daniels, Alan H. J Clin Med Article (1) Background: Length of stay (LOS) is a commonly reported metric used to assess surgical success, patient outcomes, and economic impact. The focus of this study is to use a variety of machine learning algorithms to reliably predict whether a patient undergoing posterior spinal fusion surgery treatment for Adult Spine Deformity (ASD) will experience a prolonged LOS. (2) Methods: Patients undergoing treatment for ASD with posterior spinal fusion surgery were selected from the American College of Surgeon’s NSQIP dataset. Prolonged LOS was defined as a LOS greater than or equal to 9 days. Data was analyzed with the Logistic Regression, Decision Tree, Random Forest, XGBoost, and Gradient Boosting functions in Python with the Sci-Kit learn package. Prediction accuracy and area under the curve (AUC) were calculated. (3) Results: 1281 posterior patients were analyzed. The five algorithms had prediction accuracies between 68% and 83% for posterior cases (AUC: 0.566–0.821). Multivariable regression indicated that increased Work Relative Value Units (RVU), elevated American Society of Anesthesiologists (ASA) class, and longer operating times were linked to longer LOS. (4) Conclusions: Machine learning algorithms can predict if patients will experience an increased LOS following ASD surgery. Therefore, medical resources can be more appropriately allocated towards patients who are at risk of prolonged LOS. MDPI 2021-09-09 /pmc/articles/PMC8471961/ /pubmed/34575182 http://dx.doi.org/10.3390/jcm10184074 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
Zhang, Andrew S
Veeramani, Ashwin
Quinn, Matthew S.
Alsoof, Daniel
Kuris, Eren O.
Daniels, Alan H.
Machine Learning Prediction of Length of Stay in Adult Spinal Deformity Patients Undergoing Posterior Spine Fusion Surgery
title Machine Learning Prediction of Length of Stay in Adult Spinal Deformity Patients Undergoing Posterior Spine Fusion Surgery
title_full Machine Learning Prediction of Length of Stay in Adult Spinal Deformity Patients Undergoing Posterior Spine Fusion Surgery
title_fullStr Machine Learning Prediction of Length of Stay in Adult Spinal Deformity Patients Undergoing Posterior Spine Fusion Surgery
title_full_unstemmed Machine Learning Prediction of Length of Stay in Adult Spinal Deformity Patients Undergoing Posterior Spine Fusion Surgery
title_short Machine Learning Prediction of Length of Stay in Adult Spinal Deformity Patients Undergoing Posterior Spine Fusion Surgery
title_sort machine learning prediction of length of stay in adult spinal deformity patients undergoing posterior spine fusion surgery
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8471961/
https://www.ncbi.nlm.nih.gov/pubmed/34575182
http://dx.doi.org/10.3390/jcm10184074
work_keys_str_mv AT zhangandrews machinelearningpredictionoflengthofstayinadultspinaldeformitypatientsundergoingposteriorspinefusionsurgery
AT veeramaniashwin machinelearningpredictionoflengthofstayinadultspinaldeformitypatientsundergoingposteriorspinefusionsurgery
AT quinnmatthews machinelearningpredictionoflengthofstayinadultspinaldeformitypatientsundergoingposteriorspinefusionsurgery
AT alsoofdaniel machinelearningpredictionoflengthofstayinadultspinaldeformitypatientsundergoingposteriorspinefusionsurgery
AT kurisereno machinelearningpredictionoflengthofstayinadultspinaldeformitypatientsundergoingposteriorspinefusionsurgery
AT danielsalanh machinelearningpredictionoflengthofstayinadultspinaldeformitypatientsundergoingposteriorspinefusionsurgery