Ikawa Fusao, Ichihara Nao, Horie Nobutaka, Shiokawa Yoshiaki, Nakatomi Hirofumi, Ohkuma Hiroki, Shimamura Norihito, Ueba Tetsuya, Fukuda Hitoshi, Murayama Yuichi, Sorimachi Takatoshi, Kurita Hiroki, Suzuki Kaima, Nakahara Ichiro, Kawamata Takakazu, Ishikawa Tatsuya, Chin Masaki, Ogasawara Kuniaki, Yamaguchi Shuhei, Toyoda Kazunori, Kobayashi Shotai
Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
Department of Neurosurgery, Shimane Prefectural Central Hospital, 4-1-1 Himebara, Izumo-shi, Shimane-ken, 693- 8555, Japan.
Neurosurg Rev. 2025 Jan 14;48(1):44. doi: 10.1007/s10143-025-03193-x.
Adverse effects of advanced age and poor initial neurological status on outcomes of patients with aneurysmal subarachnoid hemorrhage (SAH) have been documented. While a predictive model of the non-linear correlation between advanced age and clinical outcome has been reported, no previous model has been validated. Therefore, we created a prediction model of the non-linear correlation between advanced age and clinical outcome by machine learning and validated it using a separate cohort. Data of aneurysmal SAH patients treated by surgical clipping or endovascular coiling between 2003 and 2019 were obtained from the Japanese Stroke Databank (derivation cohort, n = 9,657) and "Predict for Outcome Study of Aneurysmal Subarachnoid Hemorrhage" (validation cohort, n = 5,085). Generalized additive models (GAMs) for poor outcome (modified Rankin Scale score ≥ 3 at discharge) were fitted with age transformation using spline curves for each World Federation of Neurological Societies grade. The discrimination property and calibration plot of unadjusted and adjusted models were assessed using the validation cohort. The derivation and validation cohorts included 3,610 and 3,251 patients, respectively. Regarding discrimination, areas under the receiver operating characteristic curve for the derivation and validation cohorts were 0.835 and 0.827, respectively, in the unadjusted model and 0.844 and 0.836, respectively, in the adjusted model. An unbiased correlation was confirmed between predicted and observed probabilities of poor outcomes. GAM could help visualize the correlation between age and clinical outcomes. Our prediction model can quantitatively aid in treatment decision-making and can be effective for most diseases and treatment settings. Trial Registration: UMIN Clinical Trials Registry (Date 2/22/2022/ ID, UMIN000046282 number, R000052809 URL, https//www.umin.ac.jp/ctr/index.htm) and the Japan Registry of Clinical Trials (Date 3/28/2022 /No. jRCT1060210092 URL, https//jrct.niph.go.jp/).
高龄和初始神经功能状态不佳对动脉瘤性蛛网膜下腔出血（SAH）患者预后的不良影响已有文献记载。虽然已有报道关于高龄与临床结局之间非线性相关性的预测模型，但此前尚无模型得到验证。因此，我们通过机器学习创建了高龄与临床结局之间非线性相关性的预测模型，并使用一个独立队列对其进行验证。2003年至2019年间接受手术夹闭或血管内栓塞治疗的动脉瘤性SAH患者的数据来自日本卒中数据库（推导队列，n = 9657）和“动脉瘤性蛛网膜下腔出血结局预测研究”（验证队列，n = 5085）。针对不良结局（出院时改良Rankin量表评分≥3）的广义相加模型（GAM），使用样条曲线对每个世界神经外科联盟分级进行年龄转换拟合。使用验证队列评估未调整模型和调整模型的辨别特性和校准图。推导队列和验证队列分别包括3610例和3251例患者。在辨别方面，未调整模型中推导队列和验证队列的受试者工作特征曲线下面积分别为0.835和0.827，调整模型中分别为0.844和0.836。证实了不良结局的预测概率与观察概率之间存在无偏相关性。GAM有助于直观显示年龄与临床结局之间的相关性。我们的预测模型可以在治疗决策中提供定量帮助，并且对大多数疾病和治疗情况都有效。试验注册：UMIN临床试验注册中心（日期2022年2月22日/ID，UMIN000046282编号，R000052809网址，https//www.umin.ac.jp/ctr/index.htm）和日本临床试验注册中心（日期2022年3月28日/编号jRCT1060210092网址，https//jrct.niph.go.jp/）。
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