2025/03/25 by Daniel Low, Sophie Stables, Laura Kondrotaite +2 · 1 citation
paper · doi:10.1111/vsu.14250
crossref issued 2025/03/25 · crossref published 2025/03/25 · crossref published-online 2025/03/25 · crossref created 2025/03/27 · crossref published-print 2025/05/01 · crossref deposited 2025/05/27 · crossref indexed 2026/07/31
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 (Test full ) and the XGBoost algorithm validated on the same test set with preoperative variables withheld (Test wh ). 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 Test full . XGBoost performance on Test wh 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.