Frailty Predicts Failure to Rescue Following Malignant Brain Tumor Resection: A National Surgical Quality Improvement Program Analysis of 14,721 Patients/ (2012–2020)

Abstract

Objective

Failure to rescue (FTR) is defined as mortality within 30 days following a major complication. While FTR has been studied in various brain tumor resections, its predictors in malignant brain tumor resection (mBTR) remain unexplored. This study aims to identify FTR predictors in mBTR resection patients using a frailty-driven model.

Methods

Patients undergoing craniotomy for mBTR were identified from the American College of Surgeons-National Surgical Quality Improvement Program database (2012–2020), with frailty measured by the Risk Analysis Index (RAI).

Results

Of 14,721 mBTR patients, 1275 (8.66%) developed major postoperative complications and 166 (13.01%) experienced FTR. The cohort's median age was 59 years (interquartile range: 47–68). Multivariate analysis revealed nonelective surgery (odds ratio [OR]: 1.48, 95% confidence interval [CI]: 1.02–2.16, P < 0.05) as an independent risk factor for FTR. Frailty was a significant independent predictor of FTR with mBTR, with both frail (N = 110) and very frail (N = 22) patients having a 5.34-fold and 8.10-fold higher odds of FTR, respectively (P < 0.001). Expectedly, major postoperative complications were predictive of FTR, including unplanned intubation (OR: 2.56, CI: 1.66–3.95, P < 0.001), prolonged ventilation (OR: 2.00, CI: 1.37–3.14, P < 0.01), cardiac arrest (OR: 16.64, CI: 8.20–33.74, P < 0.001), and septic shock (OR: 2.08, CI: 1.10–3.91, P < 0.05). The RAI-driven frailty model demonstrated excellent discriminatory accuracy for predicting FTR patients undergoing mBTR (c-statistic: 0.82, 95% CI: 0.79–0.85).

Conclusions

Preoperative RAI-measured frailty, alongside nonelective surgery, and major postoperative complications were significant predictors of FTR in mBTR patients. Identifying mBTR patients at risk for FTR using frailty strata may aid in preoperative neurosurgical risk stratification to optimize patients prior to surgery.