Machine-learning-based prediction of functional recovery in deep-pain-negative dogs after decompressive thoracolumbar hemilaminectomy for acute intervertebral disc extrusion

Abstract

Objective: To develop and compare machine-learning algorithms to predict recovery of ambulation after decompressive surgery for acute intervertebral disc extrusion (IVDE). Study design: Multicenter retrospective cohort study. Sample population: Deep-pain-negative dogs with acute IVDE (n = 162). Methods: Clinical variables were preprocessed for machine learning and split into independent training and test sets in an 80:20 ratio. Each model was trained and internally validated on the full test set (Testfull) and the XGBoost algorithm validated on the same test set with preoperative variables withheld (Testwh). Results: Recovery of ambulation was recorded in 86/162 dogs (53.1%) in this sample population after decompressive surgery. The XGBoost algorithm achieved the best performance with an area under the receiver operating characteristic curve (AUC) of.9502 (95% CI:.8919–.9901), an accuracy of.8906 (95% CI:.8125–.9531), a sensitivity of.8750, and a specificity of.9063 on Testfull. XGBoost performance on Testwh was decreased, with an AUC of.8271 (95% CI:.7186–.9209), an accuracy of.7187 (95% CI:.6093–.8281), a sensitivity of.5625, and a specificity of.8750. Conclusion: Machine-learning algorithms may predict outcomes accurately in deep-pain-negative dogs with IVDE after decompressive surgery. The XGBoost algorithm performed best on tabular data from this veterinary population undergoing spinal surgery. Clinical significance: Machine-learning algorithms outperform current methods of prognostication. Pending external validation, machine-learning algorithms may be useful as assistive tools for surgical decision making.