Frailty in intracranial meningioma resection: the risk analysis index demonstrates strong discrimination for predicting non-home discharge and in-hospital mortality

Michael M Covell; Joanna M Roy; Nithin Gupta; Ahmed Sami Raihane; Kranti C Rumalla; Amanda Cyntia Lima Fonseca Rodrigues; Evan Courville; Christian A Bowers

doi:10.1007/s11060-024-04703-5

Frailty in intracranial meningioma resection: the risk analysis index demonstrates strong discrimination for predicting non-home discharge and in-hospital mortality

J Neurooncol. 2024 May 7. doi: 10.1007/s11060-024-04703-5. Online ahead of print.

Authors

Michael M Covell^{1

2}, Joanna M Roy², Nithin Gupta³, Ahmed Sami Raihane², Kranti C Rumalla², Amanda Cyntia Lima Fonseca Rodrigues², Evan Courville², Christian A Bowers⁴

Affiliations

¹ School of Medicine, Georgetown University, 3900 Reservoir Road, 20007, Washington, DC, USA.
² Bowers Neurosurgical Frailty and Outcomes Data Science Lab, 84070, Sandy, UT, USA.
³ Campbell University School of Osteopathic Medicine, Lillington, NC, USA.
⁴ Bowers Neurosurgical Frailty and Outcomes Data Science Lab, 84070, Sandy, UT, USA. christianbowers4@gmail.com.

PMID: 38713325
DOI: 10.1007/s11060-024-04703-5

Abstract

Purpose: Frailty is an independent risk factor for adverse postoperative outcomes following intracranial meningioma resection (IMR). The role of the Risk Analysis Index (RAI) in predicting postoperative outcomes following IMR is nascent but may inform preoperative patient selection and surgical planning.

Methods: IMR patients from the Nationwide Inpatient Sample were identified using diagnostic and procedural codes (2019-2020). The relationship between preoperative RAI-measured frailty 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 23,230 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%). Rates of NHD (R 11.5%, N 29.7%, F 60.8%, VF 61.5%), in-hospital mortality (R 0.5%, N 1.8%, F 3.8%, VF 7.0%), eLOS (R 13.2%, N 21.5%, F 40.9%, VF 46.0%), and complications (R 7.5%, N 11.6%, F 15.7%, VF 16.0%) significantly increased with increasing frailty thresholds (p < 0.001). 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 RAI-measured frailty 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 following IMR, and may aid in preoperative risk stratification.

Keywords: Meningioma; Meningioma Resection; Nationwide Inpatient Sample; Neurosurgery; Risk analysis index.