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

Piya Chavalparit; Sirichai Wilartratsami; Borriwat Santipas; Piyalitt Ittichaiwong; Kanyakorn Veerakanjana; Panya Luksanapruksa

doi:10.31616/asj.2023.0051

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

Asian Spine J. 2023 Dec;17(6):1013-1023. doi: 10.31616/asj.2023.0051. Epub 2023 Dec 5.

Authors

Piya Chavalparit¹, Sirichai Wilartratsami², Borriwat Santipas², Piyalitt Ittichaiwong³, Kanyakorn Veerakanjana³, Panya Luksanapruksa²

Affiliations

¹ Department of Orthopaedic Surgery, Faculty of Medicine Vajira Hospital, Navamindradhiraj University, Bangkok, Thailand.
² Department of Orthopaedic Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
³ Siriraj Informatics and Data Innovation Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.

Abstract

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: Dependent ambulation; Neoplasm metastasis; Prognosis; Supervised machine learning; Surgical procedure.