Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
World Neurosurg. 2021 May;149:e427-e436. doi: 10.1016/j.wneu.2021.02.007. Epub 2021 Feb 7.
Although various predictors of adverse postoperative outcomes among patients with meningioma have been established, research has yet to develop a method for consolidating these findings to allow for predictions of adverse health care outcomes for patients diagnosed with skull base meningiomas. The objective of the present study was to develop 3 predictive algorithms that can be used to estimate an individual patient's probability of extended length of stay (LOS) in hospital, experiencing a nonroutine discharge disposition, or incurring high hospital charges after surgical resection of a skull base meningioma.
The present study used data from patients who underwent surgical resection for skull base meningiomas at a single academic institution between 2017 and 2019. Multivariate logistic regression analysis was used to predict extended LOS, nonroutine discharge, and high hospital charges, and 2000 bootstrapped samples were used to calculate an optimism-corrected C-statistic. The Hosmer-Lemeshow test was used to assess model calibration, and P < 0.05 was considered statistically significant.
A total of 245 patients were included in our analysis. Our cohort was mostly female (77.6%) and white (62.4%). Our models predicting extended LOS, nonroutine discharge, and high hospital charges had optimism-corrected C-statistics of 0.768, 0.784, and 0.783, respectively. All models showed adequate calibration (P>0.05), and were deployed via an open-access, online calculator: https://neurooncsurgery3.shinyapps.io/high_value_skull_base_calc/.
After external validation, our predictive models have the potential to aid clinicians in providing patients with individualized risk estimation for health care outcomes after meningioma surgery.
尽管已经确定了脑膜瘤患者术后不良结局的各种预测因素，但尚未研究出一种方法来整合这些发现，以便能够预测诊断为颅底脑膜瘤患者的不良医疗保健结局。本研究的目的是开发 3 种预测算法，用于估计个体患者在接受颅底脑膜瘤手术后住院时间延长、经历非常规出院处置或产生高额医院费用的概率。
本研究使用了 2017 年至 2019 年期间在一家学术机构接受颅底脑膜瘤手术切除的患者数据。使用多变量逻辑回归分析预测延长 LOS、非常规出院和高医院费用，并使用 2000 个 bootstrap 样本计算校正后的 C 统计量。使用 Hosmer-Lemeshow 检验评估模型校准，P<0.05 被认为具有统计学意义。
共有 245 名患者纳入本分析。我们的队列主要为女性（77.6%）和白人（62.4%）。我们预测延长 LOS、非常规出院和高医院费用的模型的校正后 C 统计量分别为 0.768、0.784 和 0.783。所有模型均显示出良好的校准（P>0.05），并通过一个开放访问的在线计算器进行部署：https://neurooncsurgery3.shinyapps.io/high_value_skull_base_calc/。
经过外部验证，我们的预测模型有可能帮助临床医生为脑膜瘤手术后的患者提供医疗保健结局的个体化风险估计。
