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开颅手术治疗脑膜瘤患者出院结局预测的临床工具。

Clinical tool for prognostication of discharge outcomes following craniotomy for meningioma.

机构信息

Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA.

Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.

出版信息

Clin Neurol Neurosurg. 2023 Aug;231:107838. doi: 10.1016/j.clineuro.2023.107838. Epub 2023 Jun 15.

Abstract

BACKGROUND

Patients' comorbidities might affect the immediate postoperative morbidity and discharge disposition after surgical resection of intracranial meningioma.

OBJECTIVE

To study the impact of comorbidities on outcomes and provide a web-based application to predict time to favorable discharge.

METHODS

A retrospective review of the prospectively collected national inpatient sample (NIS) database was conducted for the years 2009-2013. Time to favorable discharge was defined as hospital length of stay (LOS). A favorable discharge was defined as a discharge to home and a non-home discharge destination was defined as an unfavorable discharge. Cox proportional hazards model was built. Full model for time to discharge and separate reduced models were built.

RESULTS

Of 10,757 patients who underwent surgery for meningioma, 6554 (60%) had a favorable discharge. The median hospital LOS was 3 days (interquartile range [IQR] 2-5). In the full model, several clinical and socioeconomic factors were associated with a higher likelihood of unfavorable discharge. In the reduced model, 13 modifiable comorbidities were negatively associated with a favorable discharge except for drug abuse and obesity, which are not associated with discharge. Both models accurately predicted time to favorable discharge (c-index:0.68-0.71).

CONCLUSION

We developed a web application using robust prognostic model that accurately predicts time to favorable discharge after surgery for meningioma. Using this tool will allow physicians to calculate individual patient discharge probabilities based on their individual comorbidities and provide an opportunity to timely risk stratify and address some of the modifiable factors prior to surgery.

摘要

背景

患者的合并症可能会影响颅内脑膜瘤切除术后的即刻术后发病率和出院去向。

目的

研究合并症对结局的影响,并提供一个基于网络的应用程序来预测有利于出院的时间。

方法

对 2009 年至 2013 年期间前瞻性收集的全国住院患者样本(NIS)数据库进行回顾性研究。有利于出院的时间定义为住院时间(LOS)。有利出院定义为出院回家,非家庭出院目的地定义为不利出院。建立 Cox 比例风险模型。建立完整的出院时间模型和单独的简化模型。

结果

在 10757 例接受脑膜瘤手术的患者中,6554 例(60%)有良好的出院情况。医院 LOS 的中位数为 3 天(四分位距 [IQR] 2-5)。在全模型中,几个临床和社会经济因素与不利出院的可能性更高相关。在简化模型中,除药物滥用和肥胖外,13 种可修改的合并症与良好的出院结果呈负相关,而药物滥用和肥胖与出院无关。两个模型都准确地预测了脑膜瘤手术后有利于出院的时间(c 指数:0.68-0.71)。

结论

我们使用强大的预后模型开发了一个网络应用程序,该程序可以准确预测脑膜瘤手术后有利于出院的时间。使用该工具将使医生能够根据患者的个体合并症计算个体患者的出院概率,并为术前及时进行风险分层和解决一些可修改的因素提供机会。

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