Peer-reviewed veterinary case report
Predicting walking recovery in deep-pain-negative dogs after spinal
By Low, Daniel et al.·Published in Veterinary surgery : VS·2025·Frank. Pet Surgeons, United Kingdom·View original on PubMed →
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Original publication title: Machine-learning-based prediction of functional recovery in deep-pain-negative dogs after decompressive thoracolumbar hemilaminectomy for acute intervertebral disc extrusion.
- Species:
- dog
Plain-English summary
A group of dogs with severe back pain due to a slipped disc underwent surgery to relieve pressure on their spine. After the surgery, about 53% of these dogs were able to walk again, which is a promising outcome. Researchers used a machine-learning tool called XGBoost to help predict which dogs would recover their ability to walk after the procedure. This tool showed good accuracy in predicting recovery, suggesting it could be a helpful resource for veterinarians when deciding on treatment options for dogs with similar conditions.
People also search for: dog back pain surgery recovery · intervertebral disc extrusion treatment · dog walking again after back surgery
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) and the XGBoost algorithm validated on the same test set with preoperative variables withheld (Test). 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. XGBoost performance on Testwas 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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Search related cases →Original publication on PubMed: https://pubmed.ncbi.nlm.nih.gov/40130766/