Could Machine Learning Better Predict Postoperative C5 Palsy of Cervical Ossification of the Posterior Longitudinal Ligament?

Abstract

Study Design: This was a retrospective cohort study. Objective: The objective of this study was to investigate whether machine learning (ML) can perform better than a conventional logistic regression in predicting postoperative C5 palsy of cervical ossification of the posterior longitudinal ligament (OPLL) patients. Summary of Background Data: C5 palsy is one of the most common postoperative complications after surgical treatment of OPLL, with an incidence rate of 1.4%–18.4%. ML has recently been used to predict the outcomes of neurosurgery. To our knowledge there has not been a study to predict postoperative C5 palsy of cervical OPLL patient with ML. Methods: Four sampling methods were used for data balancing. Six ML algorithms and conventional logistic regression were used for model development. A total of 35 ML prediction model and 5 conventional logistic prediction models were generated. The performances of each model were compared with the area under the curve (AUC). Patients who underwent surgery for cervical OPLL at our institute from January 1998 to January 2012 were reviewed. Twenty-five variables of each patient were used to make a prediction model. Results: In total, 901 patients were included [651 male and 250 female, median age: 55 (49–63), mean±SD: 55.9±9.802]. Twenty-six (2.8%) patients developed postoperative C5 palsy. Age ( P =0.043), surgical method ( P =0.0112), involvement of OPLL at C1–3 ( P =0.0359), and postoperative shoulder pain ( P ≤0.001) were significantly associated with C5 palsy. Among all ML models, a model using an adaptive reinforcement learning algorithm and downsampling showed the largest AUC (0.88; 95% confidence interval: 0.79–0.96), better than that of logistic regression (0.69; 95% confidence interval: 0.43–0.94). Conclusions: The ML algorithm seems to be superior to logistic regression for predicting postoperative C5 palsy of OPLL patient after surgery with respect to AUC. Age, surgical method, and involvement of OPLL at C1–C3 were significantly associated with C5 palsy. This study demonstrates that shoulder pain immediately after surgery is closely associated with postoperative C5 palsy of OPLL patient.

Artificial intelligence: a new cutting-edge tool in spine surgery

“…McGrit et al [ 88 ] examined data from 750 to 1,200 patients undergoing low back surgery and used regression analysis to predict the Oswestry Disability Index 1 year after surgery with an accuracy of up to 84%. Kim et al [ 89 ] observed the incidence of postoperative C5 palsy in patients with OPLL and reported superior efficacy of ML algorithms with logistic regression models in the prediction of C5 palsy. Several similar studies using ML algorithms have reported outcome prediction following cervical and lumbar spine surgeries [ 90 – 92 ].…”

To infer the probability of cervical ossification of the posterior longitudinal ligament and explore its impact on cervical surgery

“…Since ML algorithms are increasingly being used in medical fields, several other studies have applied ML to validate the aspects of OPLL. For example, ML can predict C5 nerve palsy after cervical OPLL and to predict postoperative improvement of OPLL 14 , 41 . We collected 775 cervical spine surgery patients’ data and 84 complete variables to screen out relevant characteristics of cervical OPLL and construct a diagnostic model.…”

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