Department of Neurosurgery, Helsinki University Hospital and University of Helsinki, Topeliuksenkatu 5, P.B. 266, 00029 HUS, Helsinki, Finland.
Department of Emergency Care and Services, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
Acta Neurochir (Wien). 2022 Oct;164(10):2709-2717. doi: 10.1007/s00701-022-05353-0. Epub 2022 Sep 1.
Admission computed tomography (CT) scoring systems can be used to objectively quantify the severity of traumatic brain injury (TBI) and aid in outcome prediction. We aimed to externally validate the NeuroImaging Radiological Interpretation System (NIRIS) and the Helsinki CT score. In addition, we compared the prognostic performance of the NIRIS and the Helsinki CT score to the Marshall CT classification and to a clinical model.
We conducted a retrospective multicenter observational study using the Finnish Intensive Care Consortium database. We included adult TBI patients admitted in four university hospital ICUs during 2003-2013. We analyzed the CT scans using the NIRIS and the Helsinki CT score and compared the results to 6-month mortality as the primary outcome. In addition, we created a clinical model (age, Glasgow Coma Scale score, Simplified Acute Physiology Score II, presence of severe comorbidity) and combined clinical and CT models to see the added predictive impact of radiological data to conventional clinical information. We measured model performance using area under curve (AUC), Nagelkerke's R statistics, and the integrated discrimination improvement (IDI).
A total of 3031 patients were included in the analysis. The 6-month mortality was 710 patients (23.4%). Of the CT models, the Helsinki CT displayed best discrimination (AUC 0.73 vs. 0.70 for NIRIS) and explanatory variation (Nagelkerke's R 0.20 vs. 0.15). The clinical model displayed an AUC of 0.86 (95% CI 0.84-0.87). All CT models increased the AUC of the clinical model by + 0.01 to 0.87 (95% CI 0.85-0.88) and the IDI by 0.01-0.03.
In patients with TBI treated in the ICU, the Helsinki CT score outperformed the NIRIS for 6-month mortality prediction. In isolation, CT models offered only moderate accuracy for outcome prediction and clinical variables outweighing the CT-based predictors in terms of predictive performance.
入院时的计算机断层扫描（CT）评分系统可用于客观量化创伤性脑损伤（TBI）的严重程度，并有助于预测结果。我们旨在对外验证神经影像学放射学解释系统（NIRIS）和赫尔辛基 CT 评分。此外，我们将 NIRIS 和赫尔辛基 CT 评分的预后性能与马歇尔 CT 分类和临床模型进行了比较。
我们使用芬兰重症监护联盟数据库进行了回顾性多中心观察性研究。我们纳入了 2003 年至 2013 年间在四家大学医院 ICU 住院的成年 TBI 患者。我们使用 NIRIS 和赫尔辛基 CT 评分分析 CT 扫描，并将结果与 6 个月死亡率作为主要结局进行比较。此外，我们创建了一个临床模型（年龄、格拉斯哥昏迷评分、简化急性生理学评分 II、严重合并症的存在），并将临床和 CT 模型相结合，以了解放射学数据对常规临床信息的附加预测影响。我们使用曲线下面积（AUC）、Nagelkerke 的 R 统计量和综合判别改善（IDI）来衡量模型性能。
共有 3031 名患者纳入分析。6 个月死亡率为 710 例（23.4%）。在 CT 模型中，赫尔辛基 CT 显示出最佳的区分度（AUC 0.73 与 NIRIS 的 0.70）和解释变异度（Nagelkerke 的 R 0.20 与 0.15）。临床模型的 AUC 为 0.86（95%CI 0.84-0.87）。所有 CT 模型均使临床模型的 AUC 增加了 0.01 至 0.87（95%CI 0.85-0.88）和 IDI 增加了 0.01-0.03。
在 ICU 治疗的 TBI 患者中，赫尔辛基 CT 评分在 6 个月死亡率预测方面优于 NIRIS。单独来看，CT 模型对预后预测的准确性仅为中等，且临床变量在预测性能方面优于基于 CT 的预测因素。
