• World Neurosurg · Sep 2024

    Risk Analysis Index Predicts Nonhome Discharge Following Resection of Cavernous Malformations.

    • Pemla Jagtiani, Kranti Rumalla, Joanna M Roy, Shubhang Bhalla, Michael M Covell, and Christian A Bowers.
    • School of Medicine, SUNY Downstate Health Sciences University, New York, NY, USA; Bowers Neurosurgical Frailty and Outcomes Data Science Lab, Sandy, UT, USA.
    • World Neurosurg. 2024 Sep 1; 189: e681e687e681-e687.

    ObjectiveIntracranial cavernous malformations (CMs) are benign vascular lesions associated with hemorrhage, seizures, and corresponding neurological deficits. Recent evidence shows that frailty predicts neurosurgical adverse outcomes with superior discrimination compared to greater patient age. Therefore, we utilized the Risk Analysis Index (RAI) to predict adverse outcomes following cavernous malformation resection (CMR).MethodsThis retrospective study utilized the Nationwide Inpatient Sample to identify patients who underwent craniotomy for CMR (2019-2020). Multivariate analysis used RAI to assess the ability of frailty to predict nonhome discharge (NHD), extended length of stay (eLOS), and postoperative adverse outcomes. Receiver operating characteristic curve analysis evaluated the discriminatory accuracy of RAI for prediction of NHD.ResultsOne thousand two hundred CMR patients were identified. Mean patient age was 38±1.2 years, 53.3% (N=640) were female, and 58.3% (N=700) had private insurance. Patients were stratified into 4 frailty tiers based on RAI scores: "robust" (0-20, R), N=905 (80.8%); "normal" (21-30, N), N=110 (9.8%); "frail" (31-40, F), N=25 (2.2%); and "very frail" (41+, VF), N = 80 (7.1%). Increasing frailty was associated with eLOS and higher rates of NHD (P<0.05). The RAI demonstrated strong discriminatory accuracy (C-statistic=0.722) for prediction of NHD following CMR in area under the receiver operating characteristics.ConclusionsPreoperative frailty independently predicts adverse outcomes, including eLOS and NHD in patients undergoing resection of cranial CMs. Integrating RAI into preoperative frailty risk assessment may optimize risk stratification and improve patient selection and reallocate perioperative management resources for better patient outcomes.Copyright © 2024 Elsevier Inc. All rights reserved.

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