Frailty in Intracranial Meningioma Resection: The Risk Analysis Index Demonstrates Strong Discrimination for Predicting Non-Home Discharge and In-Hospital Mortality

Sunday, May 5, 2024

2:13 PM – 2:17 PM CT

Location: S405a

Presenting Author(s)

Michael M. Covell, BA

Medical Student, Class of 2026
Georgetown University School of Medicine
Kensington, MD, US

Co-Author(s)

CB

Christian A. Bowers, MD

Principal Investigator
Bowers Neurosurgical Frailty and Outcomes Data Science Lab
Albuquerque, New Mexico, United States
Evan Courville, MD

Pre-resident
Bowers Neurosurgical Frailty and Outcomes Data Science Lab
Lafayette, Louisiana, United States
AC

Amanda C L F Rodrigues, n/a

Medical Student
Bowers Neurosurgical Frailty and Outcomes Data Science Lab
Curitiba, PR, BR
JR

Joanna M. Roy, n/a

Medical Student
Topiwala National Medical College, United States
Kranti C. Rumalla, n/a

Medical Student
Feinberg School of Medicine, Northwestern University, United States
MS

Meic Schmidt, MD, MBA

Professor and Chair
University of New Mexico Hospital, United States

Disclosure(s):

Michael M. Covell: No financial relationships to disclose

Introduction: Frailty, or decreased baseline physiologic reserve, is an independent risk factor for adverse postoperative outcomes in intracranial tumor resection patients, including meningioma patients. Preoperative frailty assessment can help identify the patients with the highest surgical risk. The ability of the Risk Analysis Index (RAI), an accumulation-of-deficit-based frailty model, to predict adverse outcomes following intracranial meningioma resection (IMR), has not been extensively studied, and may aid in surgical candidate selection and inform preoperative care.

Methods: Patients undergoing IMR were queried from the Nationwide Inpatient Sample (NIS) using diagnostic and procedural codes (2019-2020). The relationship between preoperative frailty, as measured by the RAI, and primary outcomes (non-home discharge (NHD), in-hospital mortality) and secondary outcomes (extended length of stay (eLOS), complication rates) was assessed via multivariate analyses. The discriminatory accuracy of the RAI for primary outcomes was measured in area under the receiver operating characteristic (AUROC) curve analysis.

Results: A total of 22,710 IMR patients (mean age=59) were identified, with frailty statuses stratified by RAI score: 0–20 “robust” (R) (N=10,665, 45.9%), 21–30 “normal” (N) (N=8,895, 38.3%), 31–40 “frail” (F) (N=2,605, 11.2%), and 41+ “very frail” (VF) (N=1,065, 4.6%). NHD (R 11.5%, N 29.7%, F 60.8%, VF 61.5%; p< 0.001) and in-hospital mortality (R 0.5%, N 1.8%, F 3.8%, VF 7.0%; p< 0.001) increased as frailty thresholds increased. The RAI demonstrated strong discrimination for NHD (C-statistic: 0.755) and in-hospital mortality (C-statistic: 0.754) in AUROC curve analysis.

Conclusion : Increasing patient frailty, as measured by the RAI, is significantly associated with increased complication rates, eLOS, NHD, and in-hospital mortality following IMR. The RAI demonstrates strong discrimination for predicting NHD and in-hospital mortality in IMR patients. This knowledge will provide improved preoperative risk stratification and selection of surgical candidates.