Utilizing Double Machine Learning to Discern Risk Factors for Preoperative Depression Among Anterior Cervical Discectomy and Fusion (ACDF) Patients
Authors: Parrish James M. , Jenkins Nathaniel W. , Lynch Conor P. , Cha Elliot D.K. , Massel, MD Dustin H. , Patel Madhav R. , Jacob Kevin C. , Vanjani Nisheka N. , Pawlowski Hanna , Prabhu Michael C. , Singh Kern 
Published: June 2024

Background The Patient Health Questionnaire-9 (PHQ-9) is a common instrument for assessing depressive symptoms and has been validated in numerous patient populations. Cross-fitting or double machine learning (ML) is a method of variable selection that has gained increased attention for its potential to identify coefficients of interest. Among patients undergoing anterior cervical discectomy and fusion (ACDF), a better understanding of the factors associated with greater depressive symptoms may assist in identifying patients who could benefit the most from management of depressive symptoms prior to surgical intervention. Purpose In this study, we use ML to assess and identify the most significant risk factors associated with moderately severe depressive symptoms (PHQ-9≥15) among patients undergoing ACDF. Methods We prospectively collected surgical records and then conducted a retrospective review of patients undergoing single or multilevel ACDF between March 2016 and January 2019. We excluded patients if they underwent surgery due to infection, metastasis, or trauma. Demographic and baseline characteristics were recorded (Table 1). We recorded degenerative spinal diagnoses and symptoms (Table 2) and postoperative complications (Table 3). We assessed demographics, baseline characteristics, pain levels (Visual Analog Scale [VAS] arm and neck pain) and spine pathologies with a bivariate analysis to explore how much they elevated the risk of preoperative depression (Table 4). We used a cross-fit partialling-out LASSO (least absolute shrinkage and selection operator) logistic regression to estimate odds ratios (OR), confidence intervals, and to adequately control for and select significant covariates contributing to increased levels of preoperative depression (Table 5). Results 147 patients underwent single or multilevel ACDF procedures. 58% of our patient population was older than 50 years of age. The cohort was 42% female, 63% had an elevated body mass index (BMI), and 21% had a smoking history. Other comorbidities included hypertension (29.3%), diabetes (12.2%), arm pain (49.7%), and neck pain (48.3%). The rate of moderately severe baseline depressive symptoms (PHQ-9≥15) was 16.3%. The most common preoperative spinal pathologies and symptoms included myeloradiculopathy (90.5%), herniated nucleus pulposus (82.3%), weakness (8.8%), and radiculopathy (6.8%). The only postoperative complication was urinary retention (1.4%). Both urinary retention cases required brief catheter replacement and were resolved prior to discharge. On bivariate analysis, significant baseline characteristics associated with higher levels of preoperative depression (PHQ-9 ≥ 15) included: BMI ≥30 kg/m2 (OR = 2.2, p = 0.040), WC insurance (OR = 2.2, p = 0.035), VAS arm pain ≥7 (OR = 2.5, p = 0.032), and VAS neck pain ≥7 (OR = 4.1, p = 0.003). Our cross-fit partialing-out LASSO regression revealed VAS neck pain ≥7 (OR = 6.8, p = 0.002) and BMI ≥30 kg/m2 (OR = 3.0, p = 0.034) as potentially significant risk factors for preoperative depression severity. Conclusion Our study utilized DML to identify risk factors associated with elevated levels of preoperative depression among patients undergoing single or multilevel ACDFs. The most significant risk factors associated with moderately severe depression included increased neck pain and BMI. Further investigations are needed to identify potential ACDF outcomes and complications that are most associated with increased levels of preoperative depression influences.