208 Biological, Not Chronological: PhenoAge Outperforms Conventional Frailty Indices in Predicting 30-Day Morbidity After Surgical Stabilization of Spinal Cord Injury

INTRODUCTION: Surgery for acute spinal cord injury (SCI) entails considerable peri-operative risk, yet standard neurosurgical frailty tools discriminate poorly. Biological age clocks such as PhenoAge—derived from routine laboratory biomarkers—may better reflect physiological reserve. METHODS: SCI operations in the ACS-NSQIP (2016-2020) were analyzed. PhenoAge, derived from nine routine labs and trained on NHANES, and frailty scores were computed per published algorithms. Outcomes were 30-day mortality and morbidity. Multivariable logistic and Cox models were adjusted for demographics, ASA class, urgency, and procedure category. Models were compared using C-index, likelihood-ratio Δχ2, categorical net reclassification improvement (NRI), and Boruta random-forest variable importance. RESULTS: The cohort comprised 45,332 patients (median age 56 years; 53% male); 30-day morbidity and mortality were 7.8% and 0.3%, respectively. PhenoAge outperformed chronological age—mortality: HR 3.08, C-index 0.886, LR χ2 = 31.9 vs age HR 2.63, C-index 0.880; morbidity: HR 1.26, C-index 0.699, LR χ2 = 53.5 vs age HR 1.19, C-index 0.696—and, when both were entered, age lost prognostic value (mortality HR 0.71, morbidity HR 0.75). The highest PhenoAge decile showed 3.8× more complications and 67× higher mortality than the lowest. PhenoAge dominated mortality prediction (C-index 0.882, Δχ2=35) versus RAI 0.875/1, revised-RAI 0.874/5, and mFI-5 0.878/0, achieving an NRI +4.4%—≥4% higher than any comparator; morbidity results paralleled this pattern (0.695/92 vs 0.694/69, 0.693/54, 0.694/67; NRI +2.3%). Boruta ranked PhenoAge as the top predictor. CONCLUSIONS: In SCI surgery, biological age captured by PhenoAge surpasses chronological age and leading frailty scores for short-term outcome prediction. Requiring only standard pre-operative labs, PhenoAge could enhance counseling, drive targeted optimization, and refine risk adjustment in neurosurgical research.