Authors: Nikita Lakomkin; Scott Zuckerman, MD; Blaine Stannard; Eric Sussman, MD; Constantinos Hadjipanayis, MD, PhD; Joseph Cheng, MD, MS (New York, NY)
The modified frailty index (mFI) is a key measure of frailty based on demographics and is associated with complications in surgical cohorts, but has very limited validation in neurosurgery. The purpose of this study was to explore the relationship between the mFI and multiple postoperative outcomes in patients undergoing craniotomies for brain tumor resection as well as compare the mFI with other commonly used preoperative scores.
CPT codes were applied to the 2008-2014 National Surgical Quality Improvement (NSQIP) database in order to identify all patients undergoing craniotomy for the resection of primary and secondary intracranial lesions. American Society of Anesthesiologists (ASA) score, Charlson Comorbidity Index (CCI), and mFI were computed for each patient. A binary logistic regression model was used to explore the relationship between these variables and postoperative outcomes including mortality, major complications (Clavien IV), minor complications, and hospital length of stay (LOS). Other significant variables such as demographics, operative time, body mass index (BMI), and tumor location were controlled for in each model. The c-statistic was computed to assess the predictive capacity of the regression models.
A total of 17,815 patients were identified. Of these, 587 (3.3%) died within 30 days of surgery. After controlling for confounders, the mFI was an independent predictor of mortality (OR=14.5, P<0.001), major adverse events (OR=2.38, P=0.034), minor complications (OR=9.33, P<0.001), and prolonged LOS (OR=21.5, P<0.001). Patients’ CCI scores were significantly associated with mortality, but not other outcomes. The c-statistic values for the models were 0.79, 0.66, 0.761, and 0.711, respectively.
The mFI may be a valuable tool for risk stratification in brain tumor resection and provided superior discriminatory capacity compared to CCI and ASA scores. By combining key chronic conditions, past health events, and functional status, the mFI may better identify patients at risk for adverse outcomes.