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构建动脉瘤性蛛网膜下腔出血术后患者短期预后的列线图:一项两中心回顾性研究。

Constructing a nomogram for short-term prognosis in postoperative patients with aneurysmal subarachnoid hemorrhage: a two-center retrospective study.

作者信息

Wei Yingcong, Lin Xiaoyong, Lin Mingjian, Li Wencai, Luo Honghai, Zhu Gang

机构信息

Guangdong Medical University, Zhanjiang, 524023, Guangdong, China.

Dept. Neurosurgery, Gaozhou People's Hospital, Gaozhou, 525200, Guangdong, China.

出版信息

Sci Rep. 2025 Aug 1;15(1):28153. doi: 10.1038/s41598-025-11894-y.

Abstract

Aneurysmal subarachnoid hemorrhage (aSAH) is a life-threatening condition with high morbidity and mortality. Early prediction of prognosis remains challenging. This study aimed to develop a nomogram incorporating clinical and inflammatory biomarkers to predict short-term outcomes in postoperative aSAH patients. Thus optimizing the intervention strategy and improve patient quality of life. Logistic regression analysis was used to determine the single predictor of aneurysmal subarachnoid hemorrhage. Based on these independent predictors, a nomogram was created in R studio. The results showed that the aneurysmal site (3.35[95% CI, 1.05-10.66], P = 0.041), affected side (3.77[95% CI, 1.17-12.11], P = 0.026), hydrocephalus (0.03[95% CI, 0.01-0.12], P < 0.001), Hunt-Hess grade (4.13[95% CI, 1.17-14.49], P = 0.027), GCS score (4.08[95% CI, 1.02-16.25], P = 0.046), hypertension history (0.18[95% CI, 0.06-0.55], P = 0.003), WBC (3.49[95% CI, 1.06-11.56], P = 0.04), MLR (0.33[95% CI, 0.12-0.92], P = 0.035) were independent predictors. The nomogram demonstrated superior predictive accuracy compared to existing models, with lower calibration errors (training group: 0.018; validation group: 0.052) and high AUC values (0.95 and 0.901, respectively). Given the class imbalance (84.3% of patients had favorable outcomes), sensitivity analyses were performed to verify the consistency and reliability of the findings. The nomogram constructed based on aneurysm location, affected side, presence of hydrocephalus, Hunt-Hess grade, GCS score, hypertension, WBC and MLR can enables clinicians to identify high-risk aSAH patients early, facilitating targeted interventions such as anti-inflammatory therapy or hydrocephalus management. This tool may improve resource allocation and reduce disability rates in critical care settings. Thus optimizing the intervention strategy and improve patient quality of life.

摘要

动脉瘤性蛛网膜下腔出血(aSAH)是一种危及生命的疾病,具有高发病率和死亡率。早期预测预后仍然具有挑战性。本研究旨在开发一种结合临床和炎症生物标志物的列线图,以预测aSAH术后患者的短期结局。从而优化干预策略并提高患者生活质量。采用逻辑回归分析确定动脉瘤性蛛网膜下腔出血的单一预测因素。基于这些独立预测因素,在R studio中创建了列线图。结果显示,动脉瘤部位(3.35[95%CI,1.05 - 10.66],P = 0.041)、患侧(3.77[95%CI,1.17 - 12.11],P = 0.026)、脑积水(0.03[95%CI,0.01 - 0.12],P < 0.001)、Hunt - Hess分级(4.13[95%CI,1.17 - 14.49],P = 0.027)、格拉斯哥昏迷量表(GCS)评分(4.08[95%CI,1.02 - 16.25],P = 0.046)、高血压病史(0.18[95%CI,0.06 - 0.55],P = 0.003)、白细胞(WBC)(3.49[95%CI,1.06 - 11.56],P = 0.04)、单核细胞与淋巴细胞比值(MLR)(0.33[95%CI,0.12 - 0.92],P = 0.035)是独立预测因素。与现有模型相比,该列线图显示出更高的预测准确性,校准误差更低(训练组:0.018;验证组:0.052)且AUC值较高(分别为0.95和0.901)。考虑到类别不平衡(84.3%的患者预后良好),进行了敏感性分析以验证结果的一致性和可靠性。基于动脉瘤位置、患侧、脑积水的存在、Hunt - Hess分级、GCS评分、高血压、WBC和MLR构建的列线图能够使临床医生早期识别高危aSAH患者,便于进行针对性干预,如抗炎治疗或脑积水管理。该工具可能改善重症监护环境中的资源分配并降低残疾率。从而优化干预策略并提高患者生活质量。

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