Low D, Stables S, Kondrotaite L, Garland B, Rutherford S. Machine-learning-based prediction of functional recovery in deep-pain-negative dogs after decompressive thoracolumbar hemilaminectomy for acute intervertebral disc extrusion.
Vet Surg 2025;
54:665-674. [PMID:
40130766 PMCID:
PMC12063718 DOI:
10.1111/vsu.14250]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2024] [Revised: 02/18/2025] [Accepted: 03/01/2025] [Indexed: 03/26/2025]
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.
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