Development of Machine-Learning Models to Predict Ambulation Outcomes Following Spinal Metastasis Surgery

Aug 4, 2026

Retrospective cohort research develops and evaluates supervised machine-learning models for forecasting ambulation outcomes after spinal metastasis surgery. Data were collected for patients treated in a Thailand university medical center from January 2009 to November 2021, using preoperative variables and ambulatory status at 90 and 180 days postoperatively. Thirteen algorithms were trained and assessed with AUC and F1-score to quantify classification performance. Results show that extreme gradient boosting best predicted 180-day ambulation, while decision tree modeling best predicted 90-day ambulation, supporting effective prognosis-oriented decision support.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Study Design and Purpose

Methods and Data Sources

Machine-Learning Algorithms and Evaluation

Results

Conclusions

Asian Spine Journal Clinical Study Asian Spine J 2023;17(6):1013-1023 • [https://doi.org/10.31616/asj.2023.0051](https://doi.org/10.31616/asj.2023.0051) Development of Machine-Learning Models to Predict Ambulation Outcomes Following Spinal Metastasis Surgery Piya Chavalparit1, Sirichai Wilartratsami2, Borriwat Santipas2, Piyalitt Ittichaiwong3, Kanyakorn Veerakanjana3, Panya Luksanapruksa2 1Deprtment of Orthopaedic Surgery, Faculty of Medicine Vajira Hospital, Navamindradhiraj University, Bangkok, Thailand Department of Orthopaedic Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand 3Siriraj Informatics and Data Innovation Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand Study Design: Retrospective cohort study. Purpose: This study aimed to develop machine-learning algorithms to predict ambulation outcomes following surgery for spinal metastasis. Overview of Literature: Postoperative ambulation status following spinal metastasis surgery is currently difficult to predict. The improved 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 Thailand 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 classifier, 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 outcome (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. Keywords: Supervised machine learning; Prognosis; Dependent ambulation; Surgical procedure; Neoplasm metastasis Received Feb 13, 2023; Revised Jun 30, 2023; Accepted Jul 10, 2023 Corresponding author: Panya Luksanapruksa Division of Spine Surgery, Department of Orthopaedic Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University, 2 Wanglang Road, Bangkoknoi, Bangkok 10700, Thailand Tel: +66-2-419-7969, Fax: +66-2-419-7961, [E-mail: panya.luk@mahidol.ac.th](E-mail: panya.luk@mahidol.ac.th) ASJ Copyright Ⓒ 2023 by Korean Society of Spine Surgery This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work Asian Spine Journal • pISSN 1976-1902 eISSN 1976-7846 • [www.asianspinejournal.org](www.asianspinejournal.org) ([http://creativecommons.org/licenses/by-nc/4.0/](http://creativecommons.org/licenses/by-nc/4.0/)) is properly cited. 1014 Piya Chavalparit et al. Asian Spine J 2023;17(6):1013-1023 Introduction The incidence of spinal metastasis is increasing as evi

FAQs

What was the study purpose?

To develop machine-learning algorithms that predict ambulation outcomes after surgery for spinal metastasis.

What data were used to train and test the models?

Preoperative parameters and ambulatory status measured at 90 and 180 days after surgery were collected from patients treated between 2009 and 2021.

Which machine-learning algorithms performed best?

Extreme gradient boosting most accurately predicted 180-day ambulation, and the decision tree algorithm most accurately predicted 90-day ambulation based on AUC and F1-score.

Aug 4, 2026

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