Hospital-acquired infection following spinal tumor surgery: A frailty-driven pre-operative risk model

Background: Hospital-acquired infection (HAI) after spinal tumor resection surgery contributes to adverse patient outcomes and excess healthcare resource utilization. This study sought to develop a predictive model for HAI occurrence following surgery for spinal tumors. Methods: The National Surgical Quality Improvement Program (NSQIP) 2015–2019 database was queried for spinal tumor resections. Baseline demographics and preoperative clinical characteristics, including frailty, were analyzed. Frailty was measured by modified frailty score 5 (mFI-5) and risk analysis index (RAI). Univariate and multivariable analyses were performed to identify independent risk factors for HAI occurrence. A logit-based predictive model for HAI occurrence was designed and discriminative power was assessed via receiver operating characteristic (ROC) analysis. Results: Of 5883 patients undergoing spinal tumor surgery, HAI occurred in 574 (9.8 %). The HAI (vs. non-HAI) cohort was older and frailer with higher rates of preoperative functional dependence, chronic steroid use, chronic lung disease, coagulopathy, diabetes, hypertension, tobacco smoking, unintentional weight loss, and hypoalbuminemia (all P < 0.05). In multivariable analysis, independent predictors of HAI occurrence included severe frailty (mFI-5, OR: 2.3, 95 % CI: 1.1–5.2, P = 0.035), nonelective surgery (OR: 1.7, 95 % CI: 1.1–2.4, P = 0.007), and hypoalbuminemia (OR: 1.5, 95 % CI: 1.1–2.2, P = 0.027). A logistic regression model with frailty score alongside age, race, BMI, elective vs. non-elective surgery, and pre-operative labs have predicted HAI occurrence with a C-statistic of 0.68 (95 % CI: 0.64–0.72). Conclusions: HAI occurrence after spinal tumor surgery can be predicted by standardized frailty metrics, mFI-5 and RAI-rev, alongside routinely measured preoperative characteristics (demographics, comorbidities, pre-operative labs).