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Could Machine Learning Better Predict Postoperative C5 Palsy of Cervical Ossification of the Posterior Longitudinal Ligament?
This was a retrospective cohort study. OBJECTIVE: The objective of this study was to investigate whether machine learning (ML) can perform better than a conventional logistic regression in predicting postoperative C5 palsy of cervical ossification of the posterior longitudinal ligament (OPLL) patien...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9162065/ https://www.ncbi.nlm.nih.gov/pubmed/35020623 http://dx.doi.org/10.1097/BSD.0000000000001295 |
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author | Kim, Soo Heon Lee, Sun Ho Shin, Dong Ah |
author_facet | Kim, Soo Heon Lee, Sun Ho Shin, Dong Ah |
author_sort | Kim, Soo Heon |
collection | PubMed |
description | This was a retrospective cohort study. OBJECTIVE: The objective of this study was to investigate whether machine learning (ML) can perform better than a conventional logistic regression in predicting postoperative C5 palsy of cervical ossification of the posterior longitudinal ligament (OPLL) patients. SUMMARY OF BACKGROUND DATA: C5 palsy is one of the most common postoperative complications after surgical treatment of OPLL, with an incidence rate of 1.4%–18.4%. ML has recently been used to predict the outcomes of neurosurgery. To our knowledge there has not been a study to predict postoperative C5 palsy of cervical OPLL patient with ML. METHODS: Four sampling methods were used for data balancing. Six ML algorithms and conventional logistic regression were used for model development. A total of 35 ML prediction model and 5 conventional logistic prediction models were generated. The performances of each model were compared with the area under the curve (AUC). Patients who underwent surgery for cervical OPLL at our institute from January 1998 to January 2012 were reviewed. Twenty-five variables of each patient were used to make a prediction model. RESULTS: In total, 901 patients were included [651 male and 250 female, median age: 55 (49–63), mean±SD: 55.9±9.802]. Twenty-six (2.8%) patients developed postoperative C5 palsy. Age (P=0.043), surgical method (P=0.0112), involvement of OPLL at C1–3 (P=0.0359), and postoperative shoulder pain (P≤0.001) were significantly associated with C5 palsy. Among all ML models, a model using an adaptive reinforcement learning algorithm and downsampling showed the largest AUC (0.88; 95% confidence interval: 0.79–0.96), better than that of logistic regression (0.69; 95% confidence interval: 0.43–0.94). CONCLUSIONS: The ML algorithm seems to be superior to logistic regression for predicting postoperative C5 palsy of OPLL patient after surgery with respect to AUC. Age, surgical method, and involvement of OPLL at C1–C3 were significantly associated with C5 palsy. This study demonstrates that shoulder pain immediately after surgery is closely associated with postoperative C5 palsy of OPLL patient. |
format | Online Article Text |
id | pubmed-9162065 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-91620652022-06-08 Could Machine Learning Better Predict Postoperative C5 Palsy of Cervical Ossification of the Posterior Longitudinal Ligament? Kim, Soo Heon Lee, Sun Ho Shin, Dong Ah Clin Spine Surg Primary Research This was a retrospective cohort study. OBJECTIVE: The objective of this study was to investigate whether machine learning (ML) can perform better than a conventional logistic regression in predicting postoperative C5 palsy of cervical ossification of the posterior longitudinal ligament (OPLL) patients. SUMMARY OF BACKGROUND DATA: C5 palsy is one of the most common postoperative complications after surgical treatment of OPLL, with an incidence rate of 1.4%–18.4%. ML has recently been used to predict the outcomes of neurosurgery. To our knowledge there has not been a study to predict postoperative C5 palsy of cervical OPLL patient with ML. METHODS: Four sampling methods were used for data balancing. Six ML algorithms and conventional logistic regression were used for model development. A total of 35 ML prediction model and 5 conventional logistic prediction models were generated. The performances of each model were compared with the area under the curve (AUC). Patients who underwent surgery for cervical OPLL at our institute from January 1998 to January 2012 were reviewed. Twenty-five variables of each patient were used to make a prediction model. RESULTS: In total, 901 patients were included [651 male and 250 female, median age: 55 (49–63), mean±SD: 55.9±9.802]. Twenty-six (2.8%) patients developed postoperative C5 palsy. Age (P=0.043), surgical method (P=0.0112), involvement of OPLL at C1–3 (P=0.0359), and postoperative shoulder pain (P≤0.001) were significantly associated with C5 palsy. Among all ML models, a model using an adaptive reinforcement learning algorithm and downsampling showed the largest AUC (0.88; 95% confidence interval: 0.79–0.96), better than that of logistic regression (0.69; 95% confidence interval: 0.43–0.94). CONCLUSIONS: The ML algorithm seems to be superior to logistic regression for predicting postoperative C5 palsy of OPLL patient after surgery with respect to AUC. Age, surgical method, and involvement of OPLL at C1–C3 were significantly associated with C5 palsy. This study demonstrates that shoulder pain immediately after surgery is closely associated with postoperative C5 palsy of OPLL patient. Lippincott Williams & Wilkins 2022-06 2022-01-12 /pmc/articles/PMC9162065/ /pubmed/35020623 http://dx.doi.org/10.1097/BSD.0000000000001295 Text en Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
spellingShingle | Primary Research Kim, Soo Heon Lee, Sun Ho Shin, Dong Ah Could Machine Learning Better Predict Postoperative C5 Palsy of Cervical Ossification of the Posterior Longitudinal Ligament? |
title | Could Machine Learning Better Predict Postoperative C5 Palsy of Cervical Ossification of the Posterior Longitudinal Ligament? |
title_full | Could Machine Learning Better Predict Postoperative C5 Palsy of Cervical Ossification of the Posterior Longitudinal Ligament? |
title_fullStr | Could Machine Learning Better Predict Postoperative C5 Palsy of Cervical Ossification of the Posterior Longitudinal Ligament? |
title_full_unstemmed | Could Machine Learning Better Predict Postoperative C5 Palsy of Cervical Ossification of the Posterior Longitudinal Ligament? |
title_short | Could Machine Learning Better Predict Postoperative C5 Palsy of Cervical Ossification of the Posterior Longitudinal Ligament? |
title_sort | could machine learning better predict postoperative c5 palsy of cervical ossification of the posterior longitudinal ligament? |
topic | Primary Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9162065/ https://www.ncbi.nlm.nih.gov/pubmed/35020623 http://dx.doi.org/10.1097/BSD.0000000000001295 |
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