Predictive Analysis of Social Determinants of Health in Posterior Cervical Spine Surgery

Background/Objectives: Posterior cervical decompression and fusion (PCDF) carry perioperative risks and increases postoperative healthcare utilization (HU). Traditional prediction models emphasize comorbidities and surgical factors, yet social determinants of health (SDHs) are known to also affect clinical outcomes. We applied machine learning (ML) to integrate SDHs and clinical variables in predicting 90-day readmission and HU after PCDF. Methods: We conducted a retrospective, single-institution machine learning analysis of adult patients undergoing single or multilevel PCDF (2003–2023). Models were designed using 88 clinical variables and five census-derived Social Vulnerability Index (SVI) scores. Outcomes were 90-day readmission and HU (the unweighted sum of 18 post-discharge components including urgent visits, invasive procedures, non-routine testing, and imaging). Models were trained on 50 repeated 80/20 train–test splits with training-only preprocessing and hyperparameter tuning. Results: Among 1015 patients (mean age of 64.7, 59.0% male, 97.2% with multilevel fusions), 90-day readmission was 15.5% and mean HU score was 13.6 ± 9.0. Regarding readmission, the clinical-only logistic regression model was the best predictor (AUROC 0.65). For HU, clinical-only random forest performed best (MAE: 4.80, R2: 0.326). Adding SVI to the matched clinical-plus-SDH models did not improve prediction for either 90-day readmission or healthcare utilization. Length of stay, year of surgery, and several SVI measures were prominent contributors across both SHAP analyses. Conclusions: ML models integrating SDH and clinical factors available by index discharge modestly predicted readmission and HU after PCDF. However, adding SVI did not meaningfully increase their overall performance, and the models require external validation before clinical use.