Predictive Value of Geriatric Nutritional Risk Index and Risk Analysis Index for Post-operative Outcomes in Spine Surgery Patients: A Comprehensive Analysis

Abstract

Study Design: Retrospective cohort study. Objectives: Frailty and nutritional status are predictors of adverse spine surgery outcomes. This study evaluated the predictive utility of a combined Risk Analysis Index (RAI) and Geriatric Nutritional Risk Index (GNRI) model, and introduced a compound score integrating RAI, GNRI, American Society of Anesthesiologists (ASA) classification, and Preoperative Acute Severe Condition (PACS). To develop the CGARP score, we performed multivariable logistic regression with 30-day mortality as the dependent variable and GNRI, RAI, ASA, and PACS as independent variables. Methods: Using the National Surgical Quality Improvement Program (NSQIP) database (2015-2020), we assessed predictive performance for mortality, morbidity, and length of stay in spine surgery patients. Demographics, comorbidities, and surgical risk factors were analyzed across RAI quartiles. Model performance was measured using area under receiver operating characteristic curve (AUROC). Results: Among 360 133 patients, increasing frailty and malnutrition were independently associated with worse postoperative outcomes. The RAI-GNRI model showed outcome-specific discrimination, C-statistics 0.619 (reoperation) to 0.882 (mortality). The CGARP compound model outperformed individual predictors across all outcomes, with AUROCs of 0.882 (mortality), 0.762 (non-home discharge), 0.686 (extended length of stay), 0.694 (any complication), and 0.641 (readmission). Internal bootstrapping confirmed model stability. Random Forest was the most predictive machine-learning algorithm (AUC = 0.9553). Threshold analysis using Youden’s J statistic identified 4 risk categories, correlating with stepwise increases in mortality, complications, and non-home discharge. Discussion: Frailty and nutritional risk are independently predictive of adverse spine surgery outcomes. The CGARP model demonstrated superior predictive performance and provides clinically actionable risk stratification.