Recovery Trajectories of Patient-reported Outcomes After Surgery for Degenerative Cervical Myelopathy: A Bayesian...

Retrospective study.

The aim of this study was to identify recovery trajectory clusters after surgery for degenerative cervical myelopathy (DCM), as well as to determine clinical and imaging characteristics associated with functional recovery trajectories.

Accurate prediction of postsurgical neurological recovery for the individual patient with DCM is challenging due to varying patterns of functional recovery. Latent class Bayesian models can model individual patient patterns and identify groups of patients with similar phenotypes for personalized prognostication.

A prospective single-center study of 70 consecutive patients with DCM undergoing elective cervical spine decompression for DCM between 2010 and 2017 was performed. Outcomes were recorded using the modified Japanese Orthopedic Association (mJOA), Neck Disability Index (NDI), and the Short Form-36 Physical Component Score (SF-36 PCS) at 3, 6, 12, and 24 months. Recovery trajectories were constructed based on unsupervised Bayesian latent class modeling. Clinical and imaging predictors of recovery trajectories were also determined.

Recovery after surgery for DCM showed 3 distinct recovery trajectory clusters for each outcome. The commonest recovery trajectory was sustained improvement for the mJOA (41.1%), stagnation for the NDI (60.3%), and stability for the SF-36 PCS (46.6%). Age, duration of symptoms, and baseline disability were the strongest predictors of each recovery trajectory. Degree of cord compression, neck pain, and intramedullary T2-hyperintensity were predictive of NDI and SF-36 PCS but not mJOA recovery trajectory. Sex was associated with the NDI recovery trajectory but not SF-36 PCS and mJOA recovery trajectories.

Using prospective data and a data-driven approach, we identified 3 distinct recovery trajectory clusters and associated factors for mJOA, NDI, and SF-36 PCS in the first 24 months after surgery for DCM. Our results can enhance personalized clinical prognostication and guide patient expectations at different time points after surgery for DCM.