Frailty as a predictor of postoperative outcomes in neurosurgery: a systematic review.

INTRODUCTION: Baseline frailty status has been utilized to predict a wide range of outcomes and guide preoperative decision making in neurosurgery. This systematic review aims to analyze existing literature on the utilization of frailty as a predictor of neurosurgical outcomes. EVIDENCE ACQUISITION: We conducted a systematic review following PRISMA guidelines. Studies that utilized baseline frailty status to predict outcomes after a neurosurgical intervention were included in this systematic review. Studies that utilized sarcopenia as the sole measure of frailty were excluded. PubMed, EMBASE, and Cochrane library was searched from inception to March 1st, 2023, to identify relevant articles. EVIDENCE SYNTHESIS: Overall, 244 studies met the inclusion criteria. The 11-factor modified frailty index (mFI-11) was the most utilized frailty measure (N.=91, 37.2%) followed by the five-factor modified Frailty Index (mFI-5) (N.=80, 32.7%). Spine surgery was the most common subspecialty (N.=131, 53.7%), followed by intracranial tumor resection (N.=57, 23.3%), and post-operative complications were the most reported outcome (N.=130, 53.2%) in neurosurgical frailty studies. The USA and the Bowers author group published the greatest number of articles within the study period (N.=176, 72.1% and N.=37, 15.2%, respectively). CONCLUSIONS: Frailty literature has grown exponentially over the years and has been incorporated into neurosurgical decision making. Although a wide range of frailty indices exist, their utility may vary according to their ability to be incorporated in the outpatient clinical setting.

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).