Integration of chronological age does not improve the performance of a mixed-effect model using comorbidity burden and frailty to predict 90-day readmission after surgery for degenerative scoliosis

Introduction: Spinal deformity surgery can improve pain, function, and mental health, but complication and readmissions rates are not trivial. Chronological age is an important component of risk assessment, but its relative contribution to readmission is only partially understood. We sought to optimize the prediction of 90-day readmission using age, comorbidity burden, frailty, or a combination of these variables in a cohort of patients undergoing surgery for degenerative scoliosis.

Methods: Patients were identified through the Healthcare Cost and Utilization Project Nationwide Readmissions Database. Frailty was assessed using the Johns Hopkins Adjusted Clinical Groups (JHACG) frailty-defining indicator. Comorbidity was assessed using the Elixhauser Comorbidity Index (ECI). Generalized linear mixed-effects models were created to predict readmission using age, frailty, ECI, or any combination. Receiver operating characteristic (ROC) curves were generated and compared using area under the curve (AUC) and DeLong's test to assess performance.

Results: We identified 8,104 patients with mean age of 62 years and 61% women. Only 11% of patients were frail with an average ECI of 3±2. Readmission rate was 9.4%, with infection representing the most common cause (3.5%). Our first model utilized age, ECI, frailty, or any combination as primary predictors. The combination of all three variables (ECI+frailty+age) performed best, however the inclusion of chronological age did not significantly improve performance compared to ECI+frailty alone (AUC 0.603 vs. 0.599, p=0.290). A second model using only age and frailty as primary predictors performed better, however the integration of chronological age worsened model performance when comparing frailty vs. frailty+age (AUC 0.747 vs. 0.743, p=0.043). Across all models, the addition of chronological age did not result in statistically significant improvements in performance.

Conclusion : Frailty as a valuable predictor of 90-day readmission. Frailty alone performed better than combinations of ECI and chronological age. Interestingly, the integration of chronological age did not result in statistically significant improvements in any model's performance. The study suggests the need to refine surgery-specific frailty instruments and standardize their inclusion into national datasets. Additionally, this provides impetus to explore biological age, rather than chronological age, as a predictor of susceptibility to adverse outcomes and potential tool for surgical risk assessment.