Machine learning algorithms for prediction of health-related quality-of-life after surgery for mild degenerative cervical myelopathy.

BACKGROUND

Degenerative cervical myelopathy (DCM) is the most common cause of spinal cord dysfunction worldwide. Current guidelines recommend management based on the severity of myelopathy, measured by the modified Japanese Orthopedic Association (mJOA) score. Patients with moderate to severe myelopathy, defined by an mJOA below 15, are recommended to undergo surgery. However, the management for mild myelopathy (mJOA between 15 and 17) is controversial since the response to surgery is more heterogeneous.

PURPOSE

To develop machine learning algorithms predicting phenotypes of mild myelopathy patients that would benefit most from surgery.

STUDY DESIGN

Retrospective subgroup analysis of prospectively collected data.

PATIENT SAMPLES

Data were obtained from 193 mild DCM patients who underwent surgical decompression and were enrolled in the multicenter AOSpine CSM clinical trials.

OUTCOME MEASURES

The mJOA score, an assessment of functional status, was used to isolate patients with mild DCM. The primary outcome measures were change from baseline for the Short Form-36 (SF-36) mental component summary (MCS) and physical component summary (PCS) at 1-year postsurgery. These changes were dichotomized according to whether they exceeded the minimal clinically important difference.

METHODS

The data were split into training (75%) and testing (25%) sets. Model predictors included baseline demographic variables and clinical presentation. Seven machine learning algorithms and a logistic regression model were trained and optimized using the training set, and their performances were evaluated using the testing set. For each outcome (improvement in MCS or PCS), the machine learning algorithm with the greatest area under the curve (AUC) on the training set was selected for further analysis.

RESULTS

The generalized boosted model (GBM) and earth models performed well in the prediction of significant improvement in MCS and PCS respectively, with AUCs of 0.72 to 0.78 on the training set. This performance was replicated on the testing set, in which the GBM and earth models showed AUCs of 0.77 and 0.78, respectively, as well as fair to good calibration across the predicted range of probabilities. Female patients with a low initial MCS were less likely to experience significant improvement in MCS than males. The presence of certain signs and symptoms (eg, lower limb spasticity, clumsy hands) were also predictive of worse outcome.

CONCLUSIONS

Machine learning models showed good predictive power and provided information about the phenotypes of mild DCM patients most likely to benefit from surgical intervention. Overall, machine learning may be a useful tool for management of mild DCM, though external validation and prospective analysis should be performed to better solidify its role.