Development of Machine-Learning Models to Predict Ambulation Outcomes Following Spinal Metastasis Surgery
Issued Date
2023-01-01
Resource Type
ISSN
19761902
eISSN
19767846
Scopus ID
2-s2.0-85184243942
Journal Title
Asian Spine Journal
Volume
17
Issue
6
Start Page
1013
End Page
1023
Rights Holder(s)
SCOPUS
Bibliographic Citation
Asian Spine Journal Vol.17 No.6 (2023) , 1013-1023
Suggested Citation
Chavalparit P., Wilartratsami S., Santipas B., Ittichaiwong P., Veerakanjana K., Luksanapruksa P. Development of Machine-Learning Models to Predict Ambulation Outcomes Following Spinal Metastasis Surgery. Asian Spine Journal Vol.17 No.6 (2023) , 1013-1023. 1023. doi:10.31616/ASJ.2023.0051 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/97177
Title
Development of Machine-Learning Models to Predict Ambulation Outcomes Following Spinal Metastasis Surgery
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
Study Design: Retrospective cohort study. Purpose: This study aimed to develop machine-learning algorithms to predict ambulation outcomes following surgery for spinal me-tastasis. Overview of Literature: Postoperative ambulation status following spinal metastasis surgery is currently difficult to predict. The im-proved ability to predict this important postoperative outcome would facilitate management decision-making and help in determining realistic treatment goals. Methods: This retrospective study included patients who underwent spinal metastasis at a university-based medical center in Thai-land between January 2009 and November 2021. Collected data included preoperative parameters and ambulatory status 90 and 180 days following surgery. Thirteen machine-learning algorithms, namely, artificial neural network, logistic regression, CatBoost classi-fier, linear discriminant analysis, extreme gradient boosting, extra trees classifier, random forest classifier, gradient boosting classifier, light gradient boosting machine, naïve Bayes, K-neighbor classifier, Ada boost classifier, and decision tree classifier were developed to predict ambulatory status 90 and 180 days following surgery. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and F1-score. Results: In total, 167 patients were enrolled. The number of patients classified as ambulatory 90 and 180 days following surgery was 140 (81.9%) and 137 (82.0%), respectively. The extreme gradient boosting algorithm was found to most accurately predict 180-day ambulatory outcome (AUC, 0.85; F1-score, 0.90), and the decision tree algorithm most accurately predicted 90-day ambulatory out-come (AUC, 0.94; F1-score, 0.88). Conclusions: Machine-learning algorithms were effective in predicting ambulatory status following surgery for spinal metastasis. Based on our data, the extreme gradient boosting and decision tree best predicted postoperative ambulatory status 180 and 90 days after spinal metastasis surgery, respectively.