1Department of Neurosurgery and.
2Institute of Medical Informatics, University Hospital Münster, Germany.
J Neurosurg. 2020 Mar 20;134(3):1226-1236. doi: 10.3171/2020.1.JNS193186. Print 2021 Mar 1.
Various quality indicators are currently under investigation, aiming at measuring the quality of care in neurosurgery; however, the discipline currently lacks practical scoring systems for accurately assessing risk. The aim of this study was to develop three accurate, easy-to-use risk scoring systems for nosocomial infections, reoperations, and adverse events for patients with cerebral and spinal tumors.
The authors developed a semiautomatic registry with administrative and clinical data and included all patients with spinal or cerebral tumors treated between September 2017 and May 2019. Patients were further divided into development and validation cohorts. Multivariable logistic regression models were used to develop risk scores by assigning points based on β coefficients, and internal validation of the scores was performed.
In total, 1000 patients were included. An unplanned 30-day reoperation was observed in 6.8% of patients. Nosocomial infections were documented in 7.4% of cases and any adverse event in 14.5%. The risk scores comprise variables such as emergency admission, nursing care level, ECOG performance status, and inflammatory markers on admission. Three scoring systems, NoInfECT for predicting the incidence of nosocomial infections (low risk, 1.8%; intermediate risk, 8.1%; and high risk, 26.0% [p < 0.001]), LEUCut for 30-day unplanned reoperations (low risk, 2.2%; intermediate risk, 6.8%; and high risk, 13.5% [p < 0.001]), and LINC for any adverse events (low risk, 7.6%; intermediate risk, 15.7%; and high risk, 49.5% [p < 0.001]), showed satisfactory discrimination between the different outcome groups in receiver operating characteristic curve analysis (AUC ≥ 0.7).
The proposed risk scores allow efficient prediction of the likelihood of adverse events, to compare quality of care between different providers, and further provide guidance to surgeons on how to allocate preoperative care.
目前有各种质量指标正在研究中，旨在衡量神经外科的护理质量；然而，该学科目前缺乏用于准确评估风险的实用评分系统。本研究旨在为脑和脊髓肿瘤患者开发三种准确、易于使用的医院感染、再次手术和不良事件风险评分系统。
作者开发了一个具有行政和临床数据的半自动登记系统，并纳入了 2017 年 9 月至 2019 年 5 月期间接受治疗的所有脊髓或脑肿瘤患者。患者进一步分为开发和验证队列。使用多变量逻辑回归模型根据β系数分配分数来开发风险评分，并对评分进行内部验证。
共纳入 1000 例患者。有 6.8%的患者发生了计划外 30 天内再次手术。有 7.4%的病例发生了医院感染，有 14.5%的患者发生了任何不良事件。风险评分包括入院时的紧急入院、护理级别、ECOG 表现状态和炎症标志物等变量。三种评分系统，NoInfECT 用于预测医院感染的发生率（低危，1.8%；中危，8.1%；高危，26.0%[p<0.001]），LEUCut 用于预测 30 天内计划外再次手术（低危，2.2%；中危，6.8%；高危，13.5%[p<0.001]），LINC 用于预测任何不良事件（低危，7.6%；中危，15.7%；高危，49.5%[p<0.001]），在受试者工作特征曲线分析（AUC≥0.7）中显示出不同结局组之间的良好区分度。
所提出的风险评分可以有效地预测不良事件的发生可能性，用于比较不同提供者之间的护理质量，并进一步为外科医生提供如何分配术前护理的指导。
