Utility of combining frailty and comorbid disease indices in predicting outcomes following craniotomy for adult primary brain tumors: A mixed-effects model analysis using the nationwide readmissions database.

Autor: Michel M; College of Medicine, University of Florida, Gainesville, FL, USA; Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Shahrestani S; Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Boyke AE; Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Garcia CM; Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Menaker SA; Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Aguilera-Pena MP; Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Nguyen AT; Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA; College of Medicine, Charles R. Drew University of Medicine and Science, Los Angeles, CA, USA. Electronic address: alan30811@gmail.com., Yu JS; Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Black KL; Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Jazyk: angličtina
Zdroj: Clinical neurology and neurosurgery [Clin Neurol Neurosurg] 2024 Nov; Vol. 246, pp. 108521. Date of Electronic Publication: 2024 Aug 30.
DOI: 10.1016/j.clineuro.2024.108521
Abstrakt: Objective: The escalating healthcare expenditures in the United States, particularly in neurosurgery, necessitate effective tools for predicting patient outcomes and optimizing resource allocation. This study explores the utility of combining frailty and comorbidity indices, specifically the Johns Hopkins Adjusted Clinical Groups (JHACG) frailty index and the Elixhauser Comorbidity Index (ECI), in predicting hospital length of stay (LOS), non-routine discharge, and one-year readmission in patients undergoing craniotomy for benign and malignant primary brain tumors.
Methods: Leveraging the Nationwide Readmissions Database (NRD) for 2016-2019, we analyzed data from 645 patients with benign and 30,991 with malignant tumors. Frailty, ECI, and frailty + ECI were assessed as predictors using generalized linear mixed-effects models. Receiver operating characteristic (ROC) curves evaluated predictive performance.
Results: Patients in the benign tumor cohort had a mean LOS of 8.1 ± 15.1 days, and frailty + ECI outperformed frailty alone in predicting non-routine discharge (AUC 0.829 vs. 0.820, p = 0.035). The malignant tumor cohort patients had a mean LOS of 7.9 ± 9.1 days. In this cohort, frailty + ECI (AUC 0.821) outperformed both frailty (AUC 0.744, p < 0.0001) and ECI alone (AUC 0.809, p < 0.0001) in predicting hospital LOS. Frailty + ECI (AUC 0.831) also proved superior to frailty (AUC 0.809, p < 0.0001) and ECI alone (AUC 0.827, p < 0.0001) in predicting non-routine discharge location for patients with malignant tumors. All indices performed comparably to one another as a predictor of readmission in both cohorts.
Conclusion: This study highlights the synergistic predictive capacity of frailty + ECI, especially in malignant tumor cases, and further suggests that comorbid diseases may greatly influence perioperative outcomes more than frailty. Enhanced risk assessment could aid clinical decision-making, patient counseling, and resource allocation, ultimately optimizing patient outcomes.
(Copyright © 2024 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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