1Department of Neurosurgery, University of Southern California, Los Angeles, California.
2Department of Medical Engineering, California Institute of Technology, Pasadena, California.
Neurosurg Focus. 2022 May;52(5):E3. doi: 10.3171/2022.2.FOCUS21782.
Frailty embodies a state of increased medical vulnerability that is most often secondary to age-associated decline. Recent literature has highlighted the role of frailty and its association with significantly higher rates of morbidity and mortality in patients with CNS neoplasms. There is a paucity of research regarding the effects of frailty as it relates to neurocutaneous disorders, namely, neurofibromatosis type 1 (NF1). In this study, the authors evaluated the role of frailty in patients with NF1 and compared its predictive usefulness against the Elixhauser Comorbidity Index (ECI).
Publicly available 2016-2017 data from the Nationwide Readmissions Database was used to identify patients with a diagnosis of NF1 who underwent neurosurgical resection of an intracranial tumor. Patient frailty was queried using the Johns Hopkins Adjusted Clinical Groups frailty-defining indicator. ECI scores were collected in patients for quantitative measurement of comorbidities. Propensity score matching was performed for age, sex, ECI, insurance type, and median income by zip code, which yielded 60 frail and 60 nonfrail patients. Receiver operating characteristic (ROC) curves were created for complications, including mortality, nonroutine discharge, financial costs, length of stay (LOS), and readmissions while using comorbidity indices as predictor values. The area under the curve (AUC) of each ROC served as a proxy for model performance.
After propensity matching of the groups, frail patients had an increased mean ± SD hospital cost ($85,441.67 ± $59,201.09) compared with nonfrail patients ($49,321.77 ± $50,705.80) (p = 0.010). Similar trends were also found in LOS between frail (23.1 ± 14.2 days) and nonfrail (10.7 ± 10.5 days) patients (p = 0.0020). For each complication of interest, ROC curves revealed that frailty scores, ECI scores, and a combination of frailty+ECI were similarly accurate predictors of variables (p > 0.05). Frailty+ECI (AUC 0.929) outperformed using only ECI for the variable of increased LOS (AUC 0.833) (p = 0.013). When considering 1-year readmission, frailty (AUC 0.642) was outperformed by both models using ECI (AUC 0.725, p = 0.039) and frailty+ECI (AUC 0.734, p = 0.038).
These findings suggest that frailty and ECI are useful in predicting key complications, including mortality, nonroutine discharge, readmission, LOS, and higher costs in NF1 patients undergoing intracranial tumor resection. Consideration of a patient's frailty status is pertinent to guide appropriate inpatient management as well as resource allocation and discharge planning.
虚弱体现了一种更容易受到医疗影响的状态，这种状态通常是由与年龄相关的衰退引起的。最近的文献强调了虚弱的作用及其与中枢神经系统肿瘤患者发病率和死亡率显著增加的关系。关于虚弱与神经皮肤疾病（即神经纤维瘤病 1 型，NF1）的关系的研究很少。在这项研究中，作者评估了虚弱在 NF1 患者中的作用，并将其预测有用性与 Elixhauser 合并症指数（ECI）进行了比较。
利用 2016-2017 年全国再入院数据库中的公开数据，确定接受颅内肿瘤神经外科切除术的 NF1 患者。使用约翰霍普金斯调整临床组虚弱定义指标查询患者的虚弱情况。收集患者的 ECI 评分，以定量测量合并症。通过邮政编码对年龄、性别、ECI、保险类型和中位数收入进行倾向评分匹配，得出 60 名虚弱患者和 60 名非虚弱患者。使用合并症指数作为预测值，创建并发症（包括死亡率、非常规出院、财务成本、住院时间（LOS）和再入院）的接受者操作特征（ROC）曲线。ROC 曲线的曲线下面积（AUC）作为模型性能的代理。
在对两组进行倾向匹配后，虚弱患者的平均住院费用（$85,441.67 ± $59,201.09）高于非虚弱患者（$49,321.77 ± $50,705.80）（p = 0.010）。在虚弱（23.1 ± 14.2 天）和非虚弱（10.7 ± 10.5 天）患者之间，LOS 也存在类似的趋势（p = 0.0020）。对于每个感兴趣的并发症，ROC 曲线显示，虚弱评分、ECI 评分和虚弱+ECI 的组合是变量的相似准确预测因子（p > 0.05）。虚弱+ECI（AUC 0.929）在预测 LOS 增加的变量方面优于仅使用 ECI（AUC 0.833）（p = 0.013）。在考虑 1 年再入院时，虚弱（AUC 0.642）的表现逊于使用 ECI 的两种模型（AUC 0.725，p = 0.039）和虚弱+ECI（AUC 0.734，p = 0.038）。
这些发现表明，虚弱和 ECI 可用于预测 NF1 患者颅内肿瘤切除术后的关键并发症，包括死亡率、非常规出院、再入院、LOS 和更高的成本。考虑患者的虚弱状态对于指导适当的住院管理以及资源分配和出院计划很重要。
