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).