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...
Autores principales: | , , , , , |
---|---|
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 |