Preoperative risk model for perioperative stroke after intracranial tumor resection: ACS NSQIP analysis of 30,951 cases

Strategies to Assess and Manage Frailty among Patients Diagnosed with Primary Malignant Brain Tumors

OPINION STATEMENT

Frailty refers to a biologic process that results in reduced physiologic and functional reserve. Patients diagnosed with primary malignant brain tumors experience high symptom burden from tumor and tumor-directed treatments that, coupled with previous comorbidities, may contribute to frailty. Within the primary malignant brain tumor population, frailty is known to associate with mortality, higher healthcare utilization, and increased risk of postoperative complications. As such, methods to assess and manage frailty are paramount. However, there is currently no clear consensus on how to best assess and manage frailty throughout the entirety of the disease trajectory. Given the association between frailty and health outcomes, more research is needed to determine best practice protocols for the assessment and management of frailty among patients diagnosed with primary malignant brain tumors.

A Predictive Model of Failure to Rescue After Thoracolumbar Fusion

OBJECTIVE

Although failure to rescue (FTR) has been utilized as a quality-improvement metric in several surgical specialties, its current utilization in spine surgery is limited. Our study aims to identify the patient characteristics that are independent predictors of FTR among thoracolumbar fusion (TLF) patients.

METHODS

Patients who underwent TLF were identified using relevant diagnostic and procedural codes from the National Surgical Quality Improvement Program (NSQIP) database from 2011-2020. Frailty was assessed using the risk analysis index (RAI). FTR was defined as death, within 30 days, following a major complication. Univariate and multivariable analyses were used to compare baseline characteristics and early postoperative sequelae across FTR and non-FTR cohorts. Receiver operating characteristic (ROC) curve analysis was used to assess the discriminatory accuracy of the frailty-driven predictive model for FTR.

RESULTS

The study cohort (N = 15,749) had a median age of 66 years (interquartile range, 15 years). Increasing frailty, as measured by the RAI, was associated with an increased likelihood of FTR: odds ratio (95% confidence interval [CI]) is RAI 21-25, 1.3 [0.8-2.2]; RAI 26-30, 4.0 [2.4-6.6]; RAI 31-35, 7.0 [3.8-12.7]; RAI 36-40, 10.0 [4.9-20.2]; RAI 41- 45, 21.5 [9.1-50.6]; RAI ≥ 46, 45.8 [14.8-141.5]. The frailty-driven predictive model for FTR demonstrated outstanding discriminatory accuracy (C-statistic = 0.92; CI, 0.89-0.95).

CONCLUSION

Baseline frailty, as stratified by type of postoperative complication, predicts FTR with outstanding discriminatory accuracy in TLF patients. This frailty-driven model may inform patients and clinicians of FTR risk following TLF and help guide postoperative care after a major complication.

RAI-measured frailty predicts non-home discharge following metastatic brain tumor resection: national inpatient sample analysis of 20,185 patients

PURPOSE

Preoperative risk stratification for patients undergoing metastatic brain tumor resection (MBTR) is based on established tumor-, patient-, and disease-specific risk factors for outcome prognostication. Frailty, or decreased baseline physiologic reserve, is a demonstrated independent risk factor for adverse outcomes following MBTR. The present study sought to assess the impact of frailty, measured by the Risk Analysis Index (RAI), on MBTR outcomes.

METHODS

All MBTR were queried from the National Inpatient Sample (NIS) from 2019 to 2020 using diagnosis and procedural codes. The relationship between preoperative RAI frailty score and our primary outcome - non-home discharge (NHD) - and secondary outcomes - complication rates, extended length of stay (eLOS), and mortality - were analyzed via univariate and multivariable analyses. Discriminatory accuracy was tested by computation of concordance statistics in area under the receiver operating characteristic (AUROC) curve analysis.

RESULTS

There were 20,185 MBTR patients from the NIS database from 2019 to 2020. Each patient's frailty status was stratified by RAI score: 0-20 (robust): 34%, 21-30 (normal): 35.1%, 31-40 (very frail): 13.9%, 41+ (severely frail): 16.8%. Compared to robust patients, severely frail patients demonstrated increased complication rates (12.2% vs. 6.8%, p < 0.001), eLOS (37.6% vs. 13.2%, p < 0.001), NHD (52.0% vs. 20.6%, p < 0.001), and mortality (9.9% vs. 4.1%, p < 0.001). AUROC curve analysis demonstrated good discriminatory accuracy of RAI-measured frailty in predicting NHD after MBTR (C-statistic = 0.67).

CONCLUSION

Increasing RAI-measured frailty status is significantly associated with increased complication rates, eLOS, NHD, and mortality following MBTR. Preoperative frailty assessment using the RAI may aid in preoperative surgical planning and risk stratification for patient selection.