Impact of patient ethnicity, socioeconomic deprivation, and comorbidities on length of stay after cranial meningioma resections: A public healthcare perspective.
Aaminah Ashraf, Sayan Biswas, Ajay Dadhwal, Ella Snowdon, Joshua MacArthur, Ved Sarkar, Callum James Tetlow, K Joshi George
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引用次数: 0
Abstract
Background: Postoperative hospital length of stay (LOS) is crucial for assessing care quality, patient recovery, and resource management. However, data on how preoperative non-tumor variables affect LOS post-meningioma resection are scarce. We aimed to evaluate how ethnicity, comorbidities, and socioeconomic indices influence LOS after non-skull base meningioma resection.
Methods: A single tertiary center retrospective case series analysis of all patients undergoing non-skull base meningioma resection from 2013 to 2023 was conducted. Fourteen independent variables (age, ethnicity, sex, hypertension, diabetes mellitus [DM], chronic obstructive pulmonary disease [COPD], heart failure, myocardial infarction, stroke, dementia, cancer, index of multiple deprivations [IMD] decile, smoking, and alcohol status) were analyzed to predict the binary outcome of short (≤5 days) or extended (>5) LOS.
Results: Four hundred and seventy-nine patients were analyzed, with 65.8% of patients having a short LOS of ≤5 days. Patient ethnicity (hazard ratio [HR]: 1.160 [1.023-1.315], P = 0.02) and the presence of DM (HR: 0.551 [0.344-0.883], P = 0.013) and COPD (HR: 0.275 [0.088-0.859], P = 0.026) were statistically significant predictors of LOS after meningioma resection. Asian ethnic patients had the highest mean LOS compared to all other ethnicities. Patients with an IMD decile of ≤5 (with a higher degree of health deprivation) had a higher postoperative LOS compared to those with an IMD decile >5, but this was not statistically significant (P = 0.793).
Conclusion: Preoperative factors such as ethnicity, deprivation index, and comorbidities can potentially predict postoperative hospital LOS after meningioma resection. There is potential to develop decision support tools integrating these preoperative factors with peri- and post-operative data.