Department of Neurosurgery, West China Hospital, Sichuan University, No. 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China.
Neurol Sci. 2022 Apr;43(4):2449-2460. doi: 10.1007/s10072-021-05684-3. Epub 2021 Oct 25.
We aimed to build a nomogram, based on patients with spontaneous intracerebral hemorrhage (SICH), to predict the probability of mortality and morbidity at 7 days and 90 days, respectively.
We performed a retrospective study, with patients at less than 6 h from ictus admitted to the department of neurosurgery in a single institute, from January 2011 to December 2018. A total of 1036 patients with SICH were included, 486 patients (46.9%) were 47-66 years old at diagnosis, and 711 patients (68.6%) were male. The least absolute shrinkage and section operator method was performed to identify the key adverse factors predicting the outcomes in patients with SICH, and multivariate logistic regression analysis was built on these variables, and then the results were visualized by a nomogram. The discrimination of the prognostic models was measured and compared by means of Harrell's concordance index (C-index), calibration curve, area under the curve (AUC), and decision curve analysis (DCA).
Multivariate logistic regression analysis revealed that factors affecting 7-day mortality, including the following: age, therapy, Glasgow Coma Scale (GCS) admission, location, ventricle involved, hematoma volume, white blood cell (WBC), uric acid (UA), and L-lactic dehydrogenase (LDH); and factors affecting 90-day mortality, including temperature, therapy, GCS admission, ventricle involved, WBC, international normalized ratio, UA, LDH, and systolic blood pressure. The C-index for the 7-day mortality and 90-day mortality prediction nomogram was 0.9239 (95% CI = 0.9061-0.9416) and 0.9241 (95% CI = 0.9064-0.9418), respectively. The AUC of 7-day mortality was 92.4, as is true of 90-day mortality. The calibration curve and DCA indicated that nomograms in our study had a good prediction ability. For 90-day morbidity, age, marital status, and GCS at 7-day remained statistically significant in multivariate analysis. The C-index for the prediction nomogram was 0.6898 (95% CI = 0.6511-0.7285), and the calibration curve, AUC as well as DCA curve indicated that the nomogram for the prediction of good outcome demonstrated good agreement in this cohort.
Nomograms in this study revealed many novel prognostic demographic and laboratory factors, and the individualized quantitative risk estimation by this model would be more practical for treatment management and patient counseling.
本研究旨在构建一个针对自发性脑出血（SICH）患者的列线图，以分别预测 7 天和 90 天的死亡率和发病率。
我们进行了一项回顾性研究，纳入了 2011 年 1 月至 2018 年 12 月在一家医院神经外科发病 6 小时内的患者。共纳入 1036 例 SICH 患者，486 例（46.9%）患者在诊断时为 47-66 岁，711 例（68.6%）为男性。采用最小绝对收缩和选择算子法识别影响 SICH 患者结局的关键不良因素，基于这些变量进行多变量逻辑回归分析，然后通过列线图可视化结果。通过 Harrell 一致性指数（C-index）、校准曲线、曲线下面积（AUC）和决策曲线分析（DCA）来评估和比较预测模型的区分度。
多变量逻辑回归分析显示，影响 7 天死亡率的因素包括：年龄、治疗方法、格拉斯哥昏迷量表（GCS）入院评分、发病部位、脑室受累、血肿体积、白细胞（WBC）、尿酸（UA）和 L-乳酸脱氢酶（LDH）；影响 90 天死亡率的因素包括：体温、治疗方法、GCS 入院评分、脑室受累、WBC、国际标准化比值、UA、LDH 和收缩压。7 天死亡率和 90 天死亡率预测列线图的 C-index 分别为 0.9239（95%CI=0.9061-0.9416）和 0.9241（95%CI=0.9064-0.9418）。7 天死亡率的 AUC 为 92.4，90 天死亡率的 AUC 也为 92.4。校准曲线和 DCA 表明，本研究中的列线图具有良好的预测能力。对于 90 天发病率，年龄、婚姻状况和第 7 天的 GCS 在多变量分析中仍然具有统计学意义。预测列线图的 C-index 为 0.6898（95%CI=0.6511-0.7285），校准曲线、AUC 和 DCA 曲线表明，该列线图在本队列中对良好预后的预测具有良好的一致性。
本研究中的列线图揭示了许多新的预后人口统计学和实验室因素，该模型的个体化定量风险估计对于治疗管理和患者咨询更具实用性。
