Background: Aneurysmal subarachnoid hemorrhage (aSAH) leads to severe disability and functional dependence. However, no reliable method exists to predict the clinical prognosis after aSAH. Thus, this study aimed to develop a web-based dynamic nomogram to precisely evaluate the risk of poor outcomes in patients with aSAH. Methods: Clinical patient data were retrospectively analyzed at two medical centers. One center with 126 patients was used to develop the model. Least absolute shrinkage and selection operator (LASSO) analysis was used to select the optimal variables. Multivariable logistic regression was applied to identify independent prognostic factors and construct a nomogram based on the selected variables. The C-index and Hosmer-Lemeshow p-value and Brier score was used to reflect the discrimination and calibration capacities of the model. Receiver operating characteristic curve and calibration curve (1,000 bootstrap resamples) were generated for internal validation, while another center with 84 patients was used to validate the model externally. Decision curve analysis (DCA) and clinical impact curves (CICs) were used to evaluate the clinical usefulness of the nomogram. Results: Unfavorable prognosis was observed in 46 (37%) patients in the training cohort and 24 (29%) patients in the external validation cohort. The independent prognostic factors of the nomogram, including neutrophil-to-lymphocyte ratio (NLR) (p = 0.005), World Federation of Neurosurgical Societies (WFNS) grade (p = 0.002), and delayed cerebral ischemia (DCI) (p = 0.0003), were identified using LASSO and multivariable logistic regression. A dynamic nomogram (https://hu-ping.shinyapps.io/DynNomapp/) was developed. The nomogram model demonstrated excellent discrimination, with a bias-corrected C-index of 0.85, and calibration capacities (Hosmer-Lemeshow p-value, 0.412; Brier score, 0.12) in the training cohort. Application of the model to the external validation cohort yielded a C-index of 0.84 and a Brier score of 0.13. Both DCA and CIC showed a superior overall net benefit over the entire range of threshold probabilities. Conclusion: This study identified that NLR on admission, WFNS grade, and DCI independently predicted unfavorable prognosis in patients with aSAH. These factors were used to develop a web-based dynamic nomogram application to calculate the precise probability of a poor patient outcome. This tool will benefit personalized treatment and patient management and help neurosurgeons make better clinical decisions.

中文翻译：

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用于预测动脉瘤性蛛网膜下腔出血患者不良预后的外部验证动态列线图。

背景：动脉瘤性蛛网膜下腔出血 (aSAH) 会导致严重的残疾和功能依赖。然而，没有可靠的方法来预测 aSAH 后的临床预后。因此，本研究旨在开发基于网络的动态列线图，以精确评估 aSAH 患者预后不良的风险。方法：回顾性分析两个医疗中心的临床患者数据。一个拥有 126 名患者的中心用于开发该模型。使用最小绝对收缩和选择算子 (LASSO) 分析来选择最佳变量。应用多变量逻辑回归来识别独立的预后因素并基于所选变量构建列线图。C-index 和 Hosmer-Lemeshow p 值和 Brier 分数用于反映模型的辨别和校准能力。生成接收者操作特征曲线和校准曲线（1,000 次引导重新采样）用于内部验证，而另一个拥有 84 名患者的中心用于外部验证模型。决策曲线分析 (DCA) 和临床影响曲线 (CIC) 用于评估列线图的临床实用性。结果：在训练队列中 46 名 (37%) 患者和外部验证队列中 24 名 (29%) 患者中观察到不良预后。列线图的独立预后因素，包括中性粒细胞与淋巴细胞比率 (NLR) (p = 0.005)、世界神经外科学会联合会 (WFNS) 等级 (p = 0.002) 和迟发性脑缺血 (DCI) (p = 0.0003) )，使用 LASSO 和多变量逻辑回归确定。开发了动态列线图 (https://hu-ping.shinyapps.io/DynNomapp/)。列线图模型在训练队列中表现出出色的辨别力，偏差校正 C 指数为 0.85，校准能力（Hosmer-Lemeshow p 值，0.412；Brier 得分，0.12）。将该模型应用于外部验证队列产生了 0.84 的 C 指数和 0.13 的 Brier 分数。DCA 和 CIC 在整个阈值概率范围内都显示出优越的整体净收益。结论：本研究确定入院时的 NLR、WFNS 分级和 DCI 独立预测 aSAH 患者的不良预后。这些因素被用于开发基于网络的动态列线图应用程序，以计算患者预后不良的精确概率。该工具将有利于个性化治疗和患者管理，并帮助神经外科医生做出更好的临床决策。