• Jetan H Badhiwala, Christopher D Witiw, Farshad Nassiri, Jaja Blessing N R BNR Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto Western Hospital, 399 Bathurst St, Toronto, Ontario, Canada M5T 2S8., Muhammad A Akbar, Alireza Mansouri, Zamir Merali, George M Ibrahim, Jefferson R Wilson, and Michael G Fehlings.
• Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto Western Hospital, 399 Bathurst St, Toronto, Ontario, Canada M5T 2S8.
• Spine J. 2018 Dec 1; 18 (12): 2220-2231.

Background ContextPredictors of outcome after surgery for degenerative cervical myelopathy (DCM) have been determined previously through hypothesis-driven multivariate statistical models that rely on a priori knowledge of potential confounders, exclude potentially important variables because of restrictions in model building, cannot include highly collinear variables in the same model, and ignore intrinsic correlations among variables.PurposeThe present study aimed to apply a data-driven approach to identify patient phenotypes that may predict outcomes after surgery for mild DCM.Study DesignThis is a principal component analysis of data from two related prospective, multicenter cohort studies.Patient SampleThe study included patients with mild DCM, defined by a modified Japanese Orthopaedic Association score of 15-17, undergoing surgical decompression as part of the AOSpine CSM-NA or CSM-I trials.Outcome MeasuresPatient outcomes were evaluated preoperatively at baseline and at 6 months, 1 year, and 2 years after surgery. Quality of life (QOL) was evaluated by the Neck Disability Index (NDI) and Short Form-36 version 2 (SF-36v2). These are both patient self-reported measures that evaluate health-related QOL, with NDI being specific to neck conditions and SF-36v2 being a generic instrument.Materials And MethodsThe analysis included 154 patients. A heterogeneous correlation matrix was created using a combination of Pearson, polyserial, and polychoric regressions among 67 variables, which then underwent eigen decomposition. Scores of significant principal components (PCs) (with eigenvalues>1) were included in multivariate logistic regression analyses for three dichotomous outcomes of interest: achievement of the minimum clinically important difference [MCID] in (1) NDI (≤-7.5), (2) SF-36v2 Physical Component Summary (PCS) score (≥5), and (3) SF-36v2 Mental Component Summary (MCS) score (≥5).ResultsTwenty-four significant PCs accounting for 75% of the variance in the data were identified. Two PCs were associated with achievement of the MCID in NDI. The first (PC 1) was dominated by variables related to surgical approach and number of operated levels; the second (PC 21) consisted of variables related to patient demographics, severity and etiology of DCM, comorbid status, and surgical approach. Both PC 1 and PC 21 also correlated with SF-36v2 PCS score, in addition to PC 4, which described patients' physical profile, including gender, height, and weight, as well as comorbid renal disease; PC 6, which received large loadings from variables related to cardiac disease, impaired mobility, and length of surgery and recovery; and PC 9, which harbored large contributions from features of upper limb dysfunction, cardiorespiratory disease, surgical approach, and region. In addition to PC 21, a component profiling patients' socioeconomic status and support systems and degree of physical disability (PC 24) was associated with achievement of the MCID in SF-36 MCS score.ConclusionsThrough a data-driven approach, we identified several phenotypes associated with disability and physical and mental health-related QOL. Such data reduction methods may separate patient-, disease-, and treatment-related variables more accurately into clinically meaningful phenotypes that may inform patient care and recruitment into clinical trials.Copyright © 2018 Elsevier Inc. All rights reserved.

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