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

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 Test 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.